AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

The AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO
‎AWS Machine Learning Prep PRO

‎AWS Machine Learning Prep PRO
AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault

tolerance.

Recommend and implement the appropriate machine learning services and features for a given

problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

  • [D] How can we approach this problem ?
    by /u/corporatededmeat (Machine Learning) on May 26, 2022 at 12:34 pm

    You have a combined dataset consisting of 10 component datasets collected from 10 different sources. Independent models trained separately on each component dataset perform well on hold-out examples from that dataset. However, the aggregated model trained by combining the examples from all component datasets behaves weirdly. On hold-out examples from some component datasets, the aggregated model performs better than the independent models. On others, it performs worse than the independent models. During deployment, you expect to see input examples from these 10 component sources but also from many other sources which the model has not been trained on. What approach will you take to develop a model that will generalize well to examples from the seen and also the yet-unseen sources? submitted by /u/corporatededmeat [link] [comments]

  • [R] New datasets for StyleGAN
    by /u/RonMokady (Machine Learning) on May 26, 2022 at 12:06 pm

    Hi all, The Author is here. TL;DR: We show how StyleGAN can be adapted to raw unaligned images collected from the Internet. New datasets and models are available. ​ How can we adapt StyleGAN to more complicated datasets? We have witnessed that a data-centric approach is the most effective. Raw image collections downloaded from the internet contain many outlier images and are characterized by a multi-modal distribution. Therefore, we perform automatic self-supervised filtering of the training data to remove the outliers. Our key idea is to use the generator itself for the filtering. In the second step, we employ a multi-modal variant of the StyleGAN truncation trick. This allows high quality generation while preserving the remarkable editing capabilities of StyleGAN. For more details and cool gifs, check our Project Page:https://self-distilled-stylegan.github.io/ Datasets and models: https://github.com/self-distilled-stylegan/self-distilled-internet-photos The datasets also can be directly downloaded: https://github.com/rmokady/SDIP_utils Demo for image generation: https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN ​ Feel free to ask anything that comes to your mind ​ Generated Dog Generated Elephant submitted by /u/RonMokady [link] [comments]

  • [R] CNNs are Myopic
    by /u/downtownslim (Machine Learning) on May 26, 2022 at 5:59 am

    submitted by /u/downtownslim [link] [comments]

  • [R] Large Language Models are Zero-Shot Reasoners. My summary: Adding text such as "Let’s think step by step" to a prompt "elicits chain of thought from large language models across a variety of reasoning tasks".
    by /u/Wiskkey (Machine Learning) on May 26, 2022 at 1:43 am

    submitted by /u/Wiskkey [link] [comments]

  • [D] Semantic Segmentation/Remote Sensing Challenges
    by /u/incognitoacnt (Machine Learning) on May 26, 2022 at 12:02 am

    Does anyone know of any interesting Semantic Segmentation and/or Remote Sensing competition taking place this summer? Most of what I found ends in the next 1-2 weeks. submitted by /u/incognitoacnt [link] [comments]

  • [P] Scale ML experiments from JupyterLab to the cloud
    by /u/chrismarrie (Machine Learning) on May 25, 2022 at 8:59 pm

    First Medium article is out! Come see how Optumi is thinking about the shifting workflow needs of data science and machine learning professionals. https://medium.com/@optumi/scale-ml-experiments-from-jupyterlab-to-the-cloud-141bd645d8e9 submitted by /u/chrismarrie [link] [comments]

  • [D] Google Imagen authors now produce images based on your prompt!
    by /u/aifordummies (Machine Learning) on May 25, 2022 at 5:00 pm

    If you are interested in getting your text converted to an image by Google Brain Imagen use the following link: https://twitter.com/mo_norouzi/status/1529497457234780162?s=20&t=3K_M972bMeGRR2wG6kobHQ submitted by /u/aifordummies [link] [comments]

  • [D] From classification to regression and some physics analogies
    by /u/crispub (Machine Learning) on May 25, 2022 at 4:46 pm

    Hello, Here is: https://www.researchgate.net/publication/360541388_From_Classification_to_Regression_A_Note_on_Deodata_Predictors The paper describes how to adapt a set of classification algorithms in order to perform nonlinear regression. The algorithms are described with simple numerical examples. In the "Field Sampling Density" section, the described operation is akin to estimating the strength of a field. I am interested in your opinions. Thanks. submitted by /u/crispub [link] [comments]

  • [N] Pull Requests and Discussions on Hugging Face
    by /u/unofficialmerve (Machine Learning) on May 25, 2022 at 4:42 pm

    Hey, it's Merve from Hugging Face 👋 I wanted to share some big news that I hope you find useful. The 🤗 Hub now has pull requests (PR) and discussions in repositories to improve collaboration in machine learning 🥳✨ What does PR really mean here? Let’s assume you have a big PyTorch model and someone else ported it to TensorFlow, that person can contribute that to your model repository. Someone else can open a PR to improve your model, fix your machine learning demo in a Space or change anything in the dataset. This applies for model/Space/dataset (any repo) repositories on the hub. You might say this sounds familiar to GitHub. For code, GitHub works super well and we don’t want to (and it would be very inefficient to) recreate the feature set of GitHub. What we want to focus on is creating the collaboration toolset that’s optimized for ML. You can learn more about these new features here: https://huggingface.co/blog/community-update. Looking forward to your feedback and suggestions! ✨ Hope this is useful 🙂 submitted by /u/unofficialmerve [link] [comments]

  • [D] PyTorch processes taking up tons of GPU memory - any way to reduce this?
    by /u/tmuxed (Machine Learning) on May 25, 2022 at 4:41 pm

    I am running on Arch Linux 5.17.9-arch1-1 with an NVIDIA GeForce RTX 3090 GPU. I need to run multiple processes for a reinforcement learning task, where each subprocess runs the data collection (and inference) and all the samples from that are then retrieved via queues in the main process and optimized (e.g. think of PPO but distributed, like IMPALA). I am using torch.multiprocessing for this. Unfortunately, the multiple spawned subprocesses cause A LOT of overhead in terms of GPU memory being used. See below for my nvidia-smi output: | 0 N/A N/A 559025 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559026 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559027 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559028 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559029 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559030 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559031 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559032 C ...3/envs/ml/bin/python 1873MiB | | 0 N/A N/A 559033 C ...3/envs/ml/bin/python 1873MiB | So it seems like each subprocess loads the entirety of all of PyTorch into GPU memory, which seems incredibly inefficient. Is there a way to get the subprocesses to only load this once and then share it? How can I reduce the GPU footprint for each process? EDIT: Even using a basic example from the PyTorch repo I can see the same problem: Because it's not forking, it seems to be using up tons of GPU memory for each process. Can this not be fixed? submitted by /u/tmuxed [link] [comments]

  • [P] ZenML: Build vendor-agnostic, production-ready MLOps pipelines
    by /u/htahir1 (Machine Learning) on May 25, 2022 at 4:32 pm

    Hello r/MachineLearning! Some here might remember we open-sourced ZenML, a year or so ago, and started building it out in the open. Today, we're re-launching it to the world, with a brand-new look, and a sharper focus. We've spoken to hundreds of ML teams in the last year, and here is what we've found: 🐘 Getting ML into production reliably is still hard today. It takes too long, is too complicated, and not enough people know how to do it. 🦡 MLOps platforms are not the answer because they are opinionated, rigid, and slow to change. It's time for MLOps frameworks to shine, and bring structure to the ML ecosystem that is ripe for standardization. 🐼 Well-thought-out abstractions that make sense and are flexible are what the industry needs. Our launch blog post, "The Framework Way is the Best Way", goes into further detail on this. ZenML is a framework 🖼️, not a platform 🚉 , which standardizes your MLOps pipelines. With ZenML, we enable developers of all backgrounds by providing a vendor-agnostic, open-source MLOps solution that’s easy to plug into and just works. Here's how it works: Define Pipelines and Steps Define steps and pipelines in a simple SDK At the heart of ZenML, you will find our pipelines. These pipelines provide a simple interface for you to design your ML workflows in a portable and production-ready manner. Each pipeline consists of several steps, which ingest and generate artifacts. You can design these pipelines and steps using a simple Python SDK. Configure Infrastructure Via Stacks Define infrastructure configuration in a code-agnostic way via Stacks To execute these pipelines we need to use a ZenML Stack. A stack represents the configuration of your infrastructure and consists of several stand-alone components. Three components are essential to each stack. An artifact store, where you store the input and output artifacts of your steps, a metadata store, where your executions are tracked and an orchestrator, which conducts the execution of pipeline and steps. You can also add additional components to your stacks, such as experiment trackers, secret managers, model deployers, and more. One of the most important traits about the stack is that it is completely separated from your step and pipeline code. This means if you want to move from a local setup to a remote setup all you need to do is to go to our CLI, set up a different stack, and execute the same code. Maintain Extensibility and Modularity Use the built-in ones or define your own custom flavors! The good thing is, that ZenML already comes equipped with a wide variety of implementations for these components. For example, you can use Airflow or Kubeflow to orchestrate the pipelines, Seldon or MLFlow to deploy your models, and Weights and biases to track your experiments. And this list keeps growing every day. Furthermore, you can always use the simple base abstractions to create your own integrations. For example, you can write an Argo Workflows orchestrator by overriding a simple interface and registering it via the CLI. That's it for the short introduction. To see ZenML in action, go ahead, open your terminal, and type: ​ https://preview.redd.it/11b16l9r7n191.png?width=261&format=png&auto=webp&s=56073ddd06504611e17809ac47cdc11c222806b5 GitHub: https://github.com/zenml-io/zenml ZenML Website: https://zenml.io/home Docs: https://docs.zenml.io/ So, what do you think? Exactly what we need in MLOps or just another tool for you? Looking forward to your feedback in the comments below! submitted by /u/htahir1 [link] [comments]

  • [P] Looking for advice how to apply clustering to learned embeddings of user-item interactions
    by /u/the_Wallie (Machine Learning) on May 25, 2022 at 3:31 pm

    hi, I'm working on a consumer segmentation job, where the goal is to understand if there are subgroups of consumers who behave in a similar way to each other, but different from the rest of the population. My dataset contains a user interacting with an item for a period of time, with a reasonable assumption that more time spent means a more favorable view of the item (so we can take the time spent as a proxy for the user's taste). So far, I've created an ML model to learn embeddings of size 64 to represent my approximately 950k users and their interactions with the ~10k items. My original plan was to apply k-means clustering to those 64-dimensional user embeddings. However, this approach isn't yielding the degree of separation I require (e.g. the top most popular items to interact with are all the same ones for each cluster). Trying different values for k, I also get a basically entirely flat elbow graph. How should I proceed from here? I've thought of 2 options: - retraining my embeddings with a smaller size and retrying with k-means - researching an alternative clustering algorithm is there anything I haven't considered yet, but should? If no, which of these 2 approaches would you explore first, and if you prefer the latter, which algorithm(s) would you test out first? Thanks for your help! submitted by /u/the_Wallie [link] [comments]

  • [D] Different input image size when using Visual Transformers
    by /u/alkibijad (Machine Learning) on May 25, 2022 at 3:14 pm

    I have an image classification problem, and have been using ResNet. The dense layers at the end are replaced with 1x1 convolutions, making the model fully convolutional. Classification is done on 128 x 128 patches, so if the input image is 128 x 128, I'll get output size 1x1. If image is 512 x 512, the output will be 4x4. Each output element will hold the prediction to which class the patch at that position belongs to. Now I'd like to try using transformer instead of ResNet. Can a similar thing be done with Vision Transformers? Are there any examples of that being done? submitted by /u/alkibijad [link] [comments]

  • [P] Second-tier Recommender System in FunCorp
    by /u/Puzzleheaded_Egg_396 (Machine Learning) on May 25, 2022 at 10:55 am

    Matrix decomposition is not perfect for improvement of recommendation systems. for example, you will find it hard to add gender and age of a user. In this article, we describe how we implemented a second, ranking level of the model above the collaborative one, and how two-stage recommendation systems help us to apply more complex algorithm https://medium.com/@FunCorp/putting-a-two-layered-recommendation-system-into-production-b8caaf61393d submitted by /u/Puzzleheaded_Egg_396 [link] [comments]

  • [D] Does TensorFlow Lite use the DropIT method to handle intermediate tensors?
    by /u/teraRockstar (Machine Learning) on May 25, 2022 at 10:42 am

    This blog post (Optimizing TensorFlow Lite Runtime Memory) says that TensorFlow Lite employs different approaches to handle intermediate tensors which occupy large amounts of memory. Is one of them DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training method? submitted by /u/teraRockstar [link] [comments]

  • [D] ISTM results are out - First International Symposium on the Tsetlin Machine
    by /u/olegranmo (Machine Learning) on May 25, 2022 at 10:06 am

    ​ ISTM Technical Program You find the full technical program here: https://istm.no/program/ submitted by /u/olegranmo [link] [comments]

  • [P] Image Background Changer : You can change background to whatever you want.
    by /u/supercornson (Machine Learning) on May 25, 2022 at 6:02 am

    This project was made using rembg package that performs image segmentation with U^2-Net. https://reddit.com/link/uxathe/video/chhv2eewbk191/player submitted by /u/supercornson [link] [comments]

  • [Discussion] Best Affordable way for doing ML online (Colab Pro, etc)
    by /u/BigNet1356 (Machine Learning) on May 25, 2022 at 4:47 am

    I have exhausted free gcp credits and I was wondering what are the best (affordable) ways to do machine learning online. Colab Pro (10 USD per month), Kaggle, Paperspace Gradient, etc come to mind. Any thoughts on which is the best? PS: I have used Colab Free version and Kaggle before - The session timeouts that lead you to re-run the notebook from the beginning is the worst experience ever. Also, the only information I could find about Gradient was from the founder, which obviously is not very reliable. Anybody else used it? submitted by /u/BigNet1356 [link] [comments]

  • [P] fastdup: tool for curating computer vision datasets at scale
    by /u/gradientflow (Machine Learning) on May 25, 2022 at 2:48 am

    https://github.com/visualdatabase/fastdup submitted by /u/gradientflow [link] [comments]

  • [D] Google Speech to Text vs Building similar capability in house
    by /u/Mobile_Jacket_894 (Machine Learning) on May 24, 2022 at 9:53 pm

    Hey All. I hope you're well. TL;DR I'm trying to compare the costs of building vs using existing technology For those of you who have experience building speech or signal processing ML / AI? I'm looking into building a SaaS feature that require speech to text. As an MVP, we've been using Google's speech to text API which is great - with a few problems - it's cost, and accuracy. While its accuracy is quite high, its cost, if I'm calculating correctly, relatively to our pricing, would be quite high. There are also benefits in terms of building accurate models for the specific industry we'd be using it. Does anyone have any examples of what it would take to build and operate something like that (costs / number of engineers) and more importantly operating (let's say - cost per minute to in computing power / storage) submitted by /u/Mobile_Jacket_894 [link] [comments]

  • [P] Official Imagen Website by Google Brain
    by /u/margilly_ai (Machine Learning) on May 24, 2022 at 6:59 pm

    https://imagen.research.google/ submitted by /u/margilly_ai [link] [comments]

  • [P] Introducing BlindAI, an Open-source, fast and privacy-friendly AI deployment solution. Benefit from state-of-the-art AI without ever revealing your data!
    by /u/Separate-Still3770 (Machine Learning) on May 24, 2022 at 4:07 pm

    Hello everyone, We are pleased to introduce BlindAI to the AI community. BlindAI is an AI deployment solution, leveraging secure enclaves, to make remotely hosted AI models privacy friendly. Please have a look at our GitHub (https://github.com/mithril-security/blindai) to find out more! Motivation Today, most AI tools offer no privacy by design mechanisms, so when data is sent to be analysed by third parties, the data is exposed to malicious usage or potential leakage. We illustrate it below with the use of AI for voice assistants. Audio recordings are often sent to the Cloud to be analysed, leaving conversations exposed to leaks and uncontrolled usage without users’ knowledge or consent. ​ Before and after BlindAI By using BlindAI, data remains always protected as it is only decrypted inside a Trusted Execution Environment, called an enclave, whose contents are protected by hardware. While data is in clear inside the enclave, it is inaccessible to the outside thanks to isolation and memory encryption. This way, data can be processed, enriched, and analysed by AI, without exposing it to external parties. What you can do We have been able to run several state of the art models with privacy guarantees, enabling us to tackle complex scenarios, from privacy-friendly voice assistant with Wav2vec2, to confidential chest X-Ray analysis with ResNet, through document analysis with BERT. All of these models have been tested and can run with end-to-end protection under a second on an Intel(R) Xeon(R) Platinum 8370C. Model name Example use case Inference time (ms) DistilBERT Sentiment analysis 28.435 Wav2vec2 Speech to text 617.04 Facenet Facial recognition 47.135 A more detailed list of models we can deploy with privacy, with their run time, can be found here. If you like it drop a ⭐on our GitHub (https://github.com/mithril-security/blindai)! submitted by /u/Separate-Still3770 [link] [comments]

  • [D] Recent research and methods for time series forecasting
    by /u/ndalal01 (Machine Learning) on May 24, 2022 at 1:15 pm

    Recent advances in Vision and NLP are dominating the AI community at the moment. I have been trying to find out if something exciting has been done for time series forecasting recently (last five years or so). Looking for some good starting points to keep up with the latest research. Any pointers would be highly appreciated. submitted by /u/ndalal01 [link] [comments]

  • [P] What we learned by making T5-large 2X faster than Pytorch (and any autoregressive transformer)
    by /u/pommedeterresautee (Machine Learning) on May 24, 2022 at 6:49 am

    TL;DR We made autoregressive transformer based models like T5-large 2X faster than 🤗 Hugging Face Pytorch with 3 simple tricks: storing 2 computation graphs in a single Onnx file 👯: this let us have both cache and no cache support without having any duplicated weights. When cache is used, attention switch from quadratic to linear complexity (less GPU computation) and Onnx Runtime brings us kernel fusion (less memory bound ops); zero copy 💥 to retrieve output from Onnx Runtime: we leverage Cupy API to access Onnx Runtime internal CUDA arrays and expose them through Dlpack to Pytorch. It may sound a bit complex, but it let us avoid output tensors copy which limit our memory footprint and make us much faster (check notebook for other benefits of this approach); a generic tool to convert any model (whatever the architecture) to FP16: it injects random inputs in the model to detect nodes that need to be kept in FP32 because "mixed precision" is more complicated on large generative models (usual patterns don't work at large scale). notebook: https://github.com/ELS-RD/transformer-deploy/blob/main/demo/generative-model/t5.ipynb (Onnx Runtime only) project: https://github.com/ELS-RD/transformer-deploy/ For TensorRT we have our own implementation of our approach described above which helps to provide similar latency to Onnx Runtime. It's in a dedicated Python script in the same folder as the notebook. We had to work around a documented limitation. Because of that the code is slightly more complex and we wanted to keep this notebook easy to read. https://github.com/ELS-RD/transformer-deploy/blob/main/demo/generative-model/t5_tensorrt.py text generation in 2 different setups: no cache == no long seq len The challenge We plan to use large autoregressive models like T5 mainly for few shots learning but they tend to be slow. We needed something faster (including long sequences, large models, etc.), easy to deploy (no exotic/custom framework/hardware/kernel) and generic (works on most generative transformer models, NLP related or not, compatible with Onnx Runtime and TensorRT that we are using for other stuff). In most situations, performing inference with Onnx Runtime or TensorRT usually brought large improvement over Pytorch/Hugging Face implementation. In the very specific case of autoregressive languages, things are a bit more complicated. As you know (if not, check the notebook above for a longer explanation), you can accelerate an autoregressive model by caching Key/Value representations. By using a cache, for each generated token, you are switching from a quadratic complexity to a linear one in the self/cross attention modules. Only the first generated token is done without cache. Hugging Face is using this mechanism. When you export your model to Onnx using tracing, any control flow instruction is lost (including the If instruction to enable or not a cache). All the T5 inference solutions we found seem to suffer from it (a list of existing solutions and their issues is provided in the notebook). Performance analysis and next steps With our simple approach, we have made the inference latency mostly linear to the sequence length.Profiling the GPU with Nvidia Nsight shows that GPU computation capacities are mostly unused. It likely means that we are memory bounded, it would make sense as for each step, we just perform computations for a single token. Left side, no cache, GPU is very busy, right side, GPU is waiting memory bound operations (timings are wrong because of the profiler overhead). Going deeper in the analysis, Onnx Runtime profiler confirms that we are memory bounded and spend lots of time on casting to FP16/FP32. A strategy to increase performances would be to reduce the number of casting nodes (by a second pass on the graph to remove unnecessary casting nodes). Casting nodes should be easy to reduce. Second point, MatMul (the only operation where GPU computation capacities are fully used) represent a little part of the latency because now attention is computed for only one token (excepted the first one). It means that after these transformations of the computation graph, kernel fusions to reduce the number of memory bounded operations should pay off in a much bigger way than it did in the past. Hopefully such kernel fusions will land in both TensorRT and Onnx Runtime soon. Nvidia Triton server deployment will be released when Onnx Runtime 1.12 will be supported (ORT 1.12 should be released in June, and Triton... soon after ?). ​ If you are interested in these things, you can follow me on twitter: https://twitter.com/pommedeterre33 submitted by /u/pommedeterresautee [link] [comments]

  • [P] Imagen: Latest text-to-image generation model from Google Brain!
    by /u/aifordummies (Machine Learning) on May 23, 2022 at 10:13 pm

    Imagen - unprecedented photorealism × deep level of language understanding Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Human raters prefer Imagen over other models (such as DALL-E 2) in side-by-side comparisons, both in terms of sample quality and image-text alignment. https://gweb-research-imagen.appspot.com/ https://gweb-research-imagen.appspot.com/paper.pdf submitted by /u/aifordummies [link] [comments]

  • [D] Machine Learning - WAYR (What Are You Reading) - Week 138
    by /u/ML_WAYR_bot (Machine Learning) on May 22, 2022 at 9:49 pm

    This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Previous weeks : 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 Week 1 Week 11 Week 21 Week 31 Week 41 Week 51 Week 61 Week 71 Week 81 Week 91 Week 101 Week 111 Week 121 Week 131 Week 2 Week 12 Week 22 Week 32 Week 42 Week 52 Week 62 Week 72 Week 82 Week 92 Week 102 Week 112 Week 122 Week 132 Week 3 Week 13 Week 23 Week 33 Week 43 Week 53 Week 63 Week 73 Week 83 Week 93 Week 103 Week 113 Week 123 Week 133 Week 4 Week 14 Week 24 Week 34 Week 44 Week 54 Week 64 Week 74 Week 84 Week 94 Week 104 Week 114 Week 124 Week 134 Week 5 Week 15 Week 25 Week 35 Week 45 Week 55 Week 65 Week 75 Week 85 Week 95 Week 105 Week 115 Week 125 Week 135 Week 6 Week 16 Week 26 Week 36 Week 46 Week 56 Week 66 Week 76 Week 86 Week 96 Week 106 Week 116 Week 126 Week 136 Week 7 Week 17 Week 27 Week 37 Week 47 Week 57 Week 67 Week 77 Week 87 Week 97 Week 107 Week 117 Week 127 Week 137 Week 8 Week 18 Week 28 Week 38 Week 48 Week 58 Week 68 Week 78 Week 88 Week 98 Week 108 Week 118 Week 128 Week 9 Week 19 Week 29 Week 39 Week 49 Week 59 Week 69 Week 79 Week 89 Week 99 Week 109 Week 119 Week 129 Week 10 Week 20 Week 30 Week 40 Week 50 Week 60 Week 70 Week 80 Week 90 Week 100 Week 110 Week 120 Week 130 Most upvoted papers two weeks ago: /u/joyful_reader: Article 1 /u/need___username: ml-concepts.com /u/CatalyzeX_code_bot: Paper link Besides that, there are no rules, have fun. submitted by /u/ML_WAYR_bot [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on May 22, 2022 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • Monkey Patching Python Code
    by Adrian Tam (Blog) on May 21, 2022 at 2:00 pm

    Python is a dynamic scripting language. Not only does it have a dynamic type system where a variable can be assigned to one type first and changed later, but its object model is also dynamic. This allows us to modify its behavior at run time. A consequence of this is the possibility of monkey patching. The post Monkey Patching Python Code appeared first on Machine Learning Mastery.

  • Detect social media fake news using graph machine learning with Amazon Neptune ML
    by Hasan Shojaei (AWS Machine Learning Blog) on May 19, 2022 at 4:12 pm

    In recent years, social media has become a common means for sharing and consuming news. However, the spread of misinformation and fake news on these platforms has posed a major challenge to the well-being of individuals and societies. Therefore, it is imperative that we develop robust and automated solutions for early detection of fake news

  • Optimize F1 aerodynamic geometries via Design of Experiments and machine learning
    by Pablo Hermoso Moreno (AWS Machine Learning Blog) on May 19, 2022 at 4:02 pm

    FORMULA 1 (F1) cars are the fastest regulated road-course racing vehicles in the world. Although these open-wheel automobiles are only 20–30 kilometers (or 12–18 miles) per-hour faster than top-of-the-line sports cars, they can speed around corners up to five times as fast due to the powerful aerodynamic downforce they create. Downforce is the vertical force

  • Build a risk management machine learning workflow on Amazon SageMaker with no code
    by Peter Chung (AWS Machine Learning Blog) on May 19, 2022 at 3:47 pm

    Since the global financial crisis, risk management has taken a major role in shaping decision-making for banks, including predicting loan status for potential customers. This is often a data-intensive exercise that requires machine learning (ML). However, not all organizations have the data science resources and expertise to build a risk management ML workflow. Amazon SageMaker

  • Logging in Python
    by Daniel Chung (Blog) on May 18, 2022 at 8:00 pm

    Logging is a way to store information about your script and track events that occur. When writing any complex script in Python, logging is essential for debugging software as you develop it. Without logging, finding the source of a problem in your code may be extremely time consuming. After completing this tutorial, you will know: The post Logging in Python appeared first on Machine Learning Mastery.

  • Use Amazon Lex to capture street addresses
    by Brian Yost (AWS Machine Learning Blog) on May 18, 2022 at 6:18 pm

    Amazon Lex provides automatic speech recognition (ASR) and natural language understanding (NLU) technologies to transcribe user input, identify the nature of their request, and efficiently manage conversations. Lex lets you create sophisticated conversations, streamline your user experience to improve customer satisfaction (CSAT) scores, and increase containment in your contact centers. Natural, effective customer interactions require

  • Customize pronunciation using lexicons in Amazon Polly
    by Ratan Kumar (AWS Machine Learning Blog) on May 17, 2022 at 3:36 pm

    Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington

  • Personalize your machine translation results by using fuzzy matching with Amazon Translate
    by Narcisse Zekpa (AWS Machine Learning Blog) on May 16, 2022 at 5:48 pm

    A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when

  • Profiling Python Code
    by Adrian Tam (Blog) on May 14, 2022 at 10:00 am

    Profiling is a technique to figure out how time is spent in a program. With these statistics, we can find the “hot spot” of a program and think about ways of improvement. Sometimes, a hot spot in an unexpected location may hint at a bug in the program as well. In this tutorial, we will The post Profiling Python Code appeared first on Machine Learning Mastery.

  • Enhance the caller experience with hints in Amazon Lex
    by Kai Loreck (AWS Machine Learning Blog) on May 13, 2022 at 10:36 pm

    We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up

  • Run automatic model tuning with Amazon SageMaker JumpStart
    by Doug Mbaya (AWS Machine Learning Blog) on May 13, 2022 at 12:09 am

    In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). In March 2022, we also announced the support for APIs in JumpStart. JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across

  • Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart
    by Pashmeen Mistry (AWS Machine Learning Blog) on May 12, 2022 at 10:07 pm

    In the last decade, computer vision use cases have been a growing trend, especially in industries like insurance, automotive, ecommerce, energy, retail, manufacturing, and others. Customers are building computer vision machine learning (ML) models to bring operational efficiencies and automation to their processes. Such models help automate the classification of images or detection of objects

  • Intelligently search your Jira projects with Amazon Kendra Jira cloud connector
    by Shreyas Subramanian (AWS Machine Learning Blog) on May 12, 2022 at 8:37 pm

    Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely available to users is challenging due to it

  • The Intel®3D Athlete Tracking (3DAT) scalable architecture deploys pose estimation models using Amazon Kinesis Data Streams and Amazon EKS
    by Han Man (AWS Machine Learning Blog) on May 12, 2022 at 6:42 pm

    This blog post is co-written by Jonathan Lee, Nelson Leung, Paul Min, and Troy Squillaci from Intel.  In Part 1 of this post, we discussed how Intel®3DAT collaborated with AWS Machine Learning Professional Services (MLPS) to build a scalable AI SaaS application. 3DAT uses computer vision and AI to recognize, track, and analyze over 1,000

  • Moderate, classify, and process documents using Amazon Rekognition and Amazon Textract
    by Jay Rao (AWS Machine Learning Blog) on May 12, 2022 at 5:38 pm

    Many companies are overwhelmed by the abundant volume of documents they have to process, organize, and classify to serve their customers better. Examples of such can be loan applications, tax filing, and billing. Such documents are more commonly received in image formats and are mostly multi-paged and in low-quality format. To be more competitive and

  • Achieve in-vehicle comfort using personalized machine learning and Amazon SageMaker
    by Joshua Levy (AWS Machine Learning Blog) on May 11, 2022 at 4:24 pm

    This blog post is co-written by Rudra Hota and Esaias Pech from Continental AG. Many drivers have had the experience of trying to adjust temperature settings in their vehicle while attempting to keep their eyes on the road. Whether the previous driver preferred a warmer cabin temperature, or you’re now wearing warmer clothing, or the

  • Create video subtitles with Amazon Transcribe using this no-code workflow
    by Jason O'Malley (AWS Machine Learning Blog) on May 10, 2022 at 6:23 pm

    Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code

  • Utilize AWS AI services to automate content moderation and compliance
    by Lauren Mullennex (AWS Machine Learning Blog) on May 9, 2022 at 4:01 pm

    The daily volume of third-party and user-generated content (UGC) across industries is increasing exponentially. Startups, social media, gaming, and other industries must ensure their customers are protected, while keeping operational costs down. Businesses in the broadcasting and media industries often find it difficult to efficiently add ratings to content pieces and formats to comply with

  • Content moderation design patterns with AWS managed AI services
    by Nate Bachmeier (AWS Machine Learning Blog) on May 9, 2022 at 4:00 pm

    User-generated content (UGC) grows exponentially, as well as the requirements and the cost to keep content and online communities safe and compliant. Modern web and mobile platforms fuel businesses and drive user engagement through social features, from startups to large organizations. Online community members expect safe and inclusive experiences where they can freely consume and

  • Static Analyzers in Python
    by Adrian Tam (Blog) on May 9, 2022 at 5:09 am

    Static analyzers are tools that help you check your code without really running your code. The most basic form of static analyzers is the syntax highlighters in your favorite editors. If you need to compile your code (say, in C++), your compiler, such as LLVM, may also provide some static analyzer functions to warn you The post Static Analyzers in Python appeared first on Machine Learning Mastery.

  • Process larger and wider datasets with Amazon SageMaker Data Wrangler
    by Haider Naqvi (AWS Machine Learning Blog) on May 6, 2022 at 5:30 pm

    Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler can simplify your data preparation and feature engineering processes and help you with data selection, cleaning, exploration, and visualization. Data Wrangler has over 300 built-in transforms written in PySpark,

  • Fine-tune transformer language models for linguistic diversity with Hugging Face on Amazon SageMaker
    by Arnav Khare (AWS Machine Learning Blog) on May 6, 2022 at 5:22 pm

    Approximately 7,000 languages are in use today. Despite attempts in the late 19th century to invent constructed languages such as Volapük or Esperanto, there is no sign of unification. People still choose to create new languages (think about your favorite movie character who speaks Klingon, Dothraki, or Elvish). Today, natural language processing (NLP) examples are

  • Build a custom Q&A dataset using Amazon SageMaker Ground Truth to train a Hugging Face Q&A NLU model
    by Jeremy Feltracco (AWS Machine Learning Blog) on May 6, 2022 at 4:29 pm

    In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing these models has proven to be

  • Use custom vocabulary in Amazon Lex to enhance speech recognition
    by Kai Loreck (AWS Machine Learning Blog) on May 5, 2022 at 10:34 pm

    In our daily conversations, we come across new words or terms that we may not know. Perhaps these are related to a new domain that we’re just getting familiar with, and we pick these up as we understand more about the domain. For example, home loan terminology (“curtailment”), shortened words, (“refi”, “comps”), and acronyms (“HELOC”)

  • Setting Breakpoints and Exception Hooks in Python
    by Stefania Cristina (Blog) on May 5, 2022 at 4:21 pm

    There are different ways of debugging code in Python, one of which is to introduce breakpoints into the code at points where one would like to invoke a Python debugger. The statements used to enter a debugging session at different call sites depend on the version of the Python interpreter that one is working with, The post Setting Breakpoints and Exception Hooks in Python appeared first on Machine Learning Mastery.

  • Using Kaggle in Machine Learning Projects
    by Zhe Ming Chng (Blog) on May 2, 2022 at 2:02 pm

    You’ve probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those The post Using Kaggle in Machine Learning Projects appeared first on Machine Learning Mastery.

  • Techniques to Write Better Python Code
    by Adrian Tam (Blog) on April 29, 2022 at 2:47 pm

    We write a program to solve a problem or make a tool that we can repeatedly solve a similar problem. For the latter, it is inevitable that we come back to revisit the program we wrote, or someone else is reusing the program we write. There is also a chance that we will encounter data The post Techniques to Write Better Python Code appeared first on Machine Learning Mastery.

  • Take Your Machine Learning Skills Global
    by MLM Team (Blog) on April 28, 2022 at 2:48 am

    Sponsored Post In our interconnected world, a decision made thousands of miles away can have lasting consequences for entire organizations or economies. When small changes have big effects, it is unsurprising that companies and governments are turning to machine learning and AI to accurately predict risk. ​ How the Global Community is Applying Machine Learning The post Take Your Machine Learning Skills Global appeared first on Machine Learning Mastery.

  • Google Colab for Machine Learning Projects
    by Zhe Ming Chng (Blog) on April 27, 2022 at 7:39 pm

    Have you ever wanted an easy-to-configure interactive environment to run your machine learning code that came with access to GPUs for free? Google Colab is the answer you’ve been looking for. It is a convenient and easy-to-use way to run Jupyter notebooks on the cloud, and their free version comes with some limited access to The post Google Colab for Machine Learning Projects appeared first on Machine Learning Mastery.

  • Multiprocessing in Python
    by Daniel Chung (Blog) on April 25, 2022 at 2:02 pm

    When you work on a computer vision project, you probably need to preprocess a lot of image data. This is time-consuming, and it would be great if you could process multiple images in parallel. Multiprocessing is the ability of a system to run multiple processors at one time. If you had a computer with a The post Multiprocessing in Python appeared first on Machine Learning Mastery.

Download AWS machine Learning Specialty Exam Prep App on iOs

AWS machine learning certification prep
AWS machine learning certification prep

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

AWS Data analytics DAS-C01 Exam Preparation

AWS Data analytics DAS-C01 Exam Prep

The AWS Data analytics DAS-C01 Exam Prep PRO App is very similar to real exam with a Countdown timer, a Score card.

It also gives users the ability to Show/Hide Answers, learn from Cheat Sheets, Flash Cards, and includes Detailed Answers and References for more than 300 AWS Data Analytics Questions.

Various Practice Exams covering Data Collection, Data Security, Data processing, Data Analysis, Data Visualization, Data Storage and Management,
App preview:

AWS Data Analytics DAS-C01 Exam Prep PRO
‎AWS Data Analytics Sp Exam PRO

‎AWS Data Analytics Sp Exam PRO
AWS Data Analytics Exam PRO
AWS Data Analytics Exam PRO
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Data Science and Data Analytics Quiz
Data Science and Data Analytics Quiz
Developer: ‪Etienne Noumen‬
Price: £4.49
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This App provides hundreds of Quizzes covering AWS Data analytics, Data Science, Data Lakes, S3, Kinesis, Lake Formation, Athena, Kibana, Redshift, EMR, Glue, Kafka, Apache Spark, SQL, NoSQL, Python, DynamoDB, DocumentDB,  linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, Data cleansing, ETL, IoT, etc.

AWS Data Analytics Exam PRO
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Data Science and Data Analytics Quiz
Data Science and Data Analytics Quiz
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Djamgatech Cloud Education Certification: Eduflix App for Cloud Education and Certification (AWS, Azure, Google Cloud)

Cloud Education and Certification

Do you want to become a Professional DevOps Engineer, a cloud Solutions Architect, a Cloud Engineer or a modern Developer or IT Professional? The Cloud Education Certification android and iOS App is an EduFlix App for AWS, Azure, Google Cloud Certification Preparation to help you achieve your career objectives.

The App covers the following certifications:
AWS Cloud Practitioner, Azure Fundamentals, AWS Solution Architect Associate, AWS Developer Associate, Azure Administrator, Google Associate Cloud Engineer, Data Analytics, Machine Learning.

Use this App to learn and get certified for AWS, Azure and Google Cloud Platform anytime, anywhere from your phone, tablet, computer, online, offline

‎Djamgatech Pro
Developer: DjamgaTech Corp
Price: $21.99
Djamgatech PRO: AWS Azure Cert
Djamgatech PRO: AWS Azure Cert
Developer: Unknown
Price: $22.99

Features:
– Practice exams
– 1000+ Q&A updated frequently.
– 3+ Practice exams per Certification
– Scorecard / Scoreboard to track your progress
– Quizzes with score tracking, progress bar, countdown timer.
– Can only see scoreboard after completing the quiz.
– FAQs for most popular Cloud services
– Cheat Sheets
– Flashcards
– works offline

The App covers :
AWS Cloud Practitioner Exam Prep CCP CLF-C01, Azure Fundamentals AZ 900 Exam Prep, AWS Certified Solution Architect Associate SAA-C02 Exam Prep, AWS Certified Developer Associate DVA-C01 Exam Prep, Azure Administrator AZ 104 Exam Prep, Google Associate Cloud Engineer Exam Prep, Data Analytics for AWS DAS-C01, Machine Learning for AWS and Google.

Get the App at the iOS App store here:

The Netflix of Cloud Education and Certification
Cloud Eduflix App

The App covers the following cloud categories:
AWS Technology, AWS Security and Compliance, AWS Cloud Concepts, AWS Billing and Pricing , AWS Design High Performing Architectures, AWS Design Cost Optimized Architectures, AWS Specify Secure Applications And Architectures, AWS Design Resilient Architecture, AWS undifferentiated heavy lifting, Development With AWS, AWS Deployment, AWS Security, AWS Monitoring, AWS Troubleshooting, AWS Refactoring, Azure Pricing and Support, Azure Cloud Concepts , Azure Identity, governance, and compliance, Azure Services , Implement and Manage Azure Storage, Deploy and Manage Azure Compute Resources, Configure and Manage Azure Networking Services, Monitor and Backup Azure Resources, GCP Plan and configure a cloud solution, GCP Deploy and implement a cloud solution, GCP Ensure successful operation of a cloud solution, GCP Configure access and security, GCP Setting up a cloud solution environment, AWS Incident Response, AWS Logging and Monitoring, AWS Infrastructure Security, AWS Identity and Access Management, AWS Data Protection, AWS Data Engineering, AWS Exploratory Data Analysis, AWS Modeling, AWS Machine Learning Implementation and Operations, GCP Frame ML problems, GCP Architect ML solutions, GCP Prepare and process data, GCP Develop ML models, GCP Automate & orchestrate ML pipelines, GCP Monitor, optimize, and maintain ML solutions, etc…

AWS Autoscaling , RDS, Aurora, Route 53, Amazon CodeGuru, Amazon Bracket, AWS Billing and Pricing, Simply Monthly Calculator, cost calculator, Ec2 pricing on-demand, AWS Pricing, Pay As You Go, No Upfront Cost, Cost Explorer, AWS Organizations, Consolidated billing, Instance Scheduler, on-demand instances, Reserved instances, Spot Instances, CloudFront, Workspace, S3 storage classes, Regions, Availability Zones, Placement Groups, lightsail, Redshift, EC2 G4ad instances, EMR, DAAS, PAAS, IAAS, SAAS, Machine Learning, Key Pairs, CloudFormation, Amazon Macie, Textract, Glacier Deep Archive, 99.999999999% durability, Codestar, AWS X-Ray, AWS CUR, AWS Pricing Calculator, Instance metadata, Instance userdata, SNS, Desktop As A Service, EC2 for Mac, Kubernetes, Containers, Cluster, IAM, BigQuery, Bigtable, Pub/Sub, App Engine, SAA undifferentiated heavy lifting, flow logs, Azure Pricing and Support, Azure Cloud Concepts, consumption-based mode, management groups, resources and RG, Geographic distribution concepts such as Azure regions, region pairs, and AZ Internet of Things (IoT) Hub, IoT Central, and Azure Sphere, Azure Synapse Analytics, HDInsight, and Azure Databricks, Azure Machine Learning, Cognitive Services and Azure Bot Service, Serverless computing solutions that include Azure Functions and Logic Apps, Azure DevOps, GitHub, GitHub Actions, and Azure DevTest Labs, Azure Mobile, Azure Advisor, Azure Resource Manager (ARM) templates, Azure Security, Privacy and Workloads, General security and network security, Azure security features, Azure Security Centre, policy compliance, security alerts, secure score, and resource hygiene, Key Vault, Azure Sentinel, Azure Dedicated Hosts, Concept of defense in depth, NSG, Azure Firewall, Azure DDoS protection, Identity, governance, Conditional Access, Multi-Factor Authentication (MFA), and Single Sign-On (SSO),Azure Services, Core Azure architectural components, Management Groups, Azure Resource Manager,
GCP, Virtual Machines, Azure App Services, Azure Container Instances (ACI), Azure Kubernetes Service (AKS), and Windows Virtual Desktop, Virtual Networks, VPN Gateway, Virtual Network peering, and ExpressRoute, CORS, CLI, pod
Container (Blob) Storage, Disk Storage, File Storage, and storage tiers, Cosmos DB, Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL, and SQL Managed Instance, Azure Marketplace,

Note and disclaimer: We are not affiliated with AWS, Azure, Microsoft or Google. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

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‎Djamgatech Pro
‎Djamgatech Pro
Developer: DjamgaTech Corp
Price: $21.99

Djamgatech PRO: AWS Azure Cert
Djamgatech PRO: AWS Azure Cert
Developer: Unknown
Price: $22.99

AWS Certified Solution Architect Associate Prep App

AWS Solution Architect Associate Training and Certification Preparation App

AWS Certified Solutions Architect – Associate  average salary

The AWS Certified Solutions Architect – Associate  average salary is  $149,446/

This blog is about the AWS Certification and Training App for Solution Architect Associate, SAA, SAA-C02, SAA-C03. The AWS Certified Solution Architect Associate Practice Exams Quiz App contain 200+ Questions and Answers updated frequently, detailed answers and references, Quizzes for each exam category, score card for each category and mock exam, Score Tracker, countdown timer, Cheat Sheets, Flash Cards, Training Videos, etc.

AWS Solution Architect Associate Training and Certification Preparation App
AWS Solution Architect Associate Training and Certification Preparation App

AWS Solutions Architect Associates SAA-C02 and SAA-C03 Certification Exam Prep

 AWS Certification and Training App for  Solution Architect Associate, SAA, SAA-C02, SAA-C03
AWS SAA SAA-C02 SAA-C03 Exam Prep on Android 

#AWS #SAAC02 #SAAC03 #SolutionsArchitect #AWSSAA #SAA #AWSCertification #AWSTraining #LearnAWS #CloudArchitect #SolutionsArchitect  #Djamgatech


AWS SAA Exam Prep App on iOs
AWS SAA Exam Prep App on android
AWS SAA Exam Prep App on Windows 10/11

AWS saa SAA-C02 Solutions Architect Associate Exam Preparation PRO
AWS SAA SAA-C02 SAA-C03 Solutions Architect Associate Exam Preparation PRO

Get the AWS  SAA-C02 / SAA-C03 Exam Prep App on:  iOS – AndroidWindows 10/11

AWS Certified Solution Architect Associate Prep App Features:

The app contains questions and answers and resources about:

  • Design High Performing Architectures,
  • Design Cost Optimized Architectures,
  • Design Secure Applications And Architectures,
  • Design Resilient Architecture,
  • Quiz with score tracking, progress bar, countdown timer and highest score savings.
  • Can only see answers after completing the quiz.
  • Show/Hide answers button option after completing quiz in each category.
  • Ability to navigate through questions for each category using next and previous button.
  • Resource info page about the answer for each category and Top 60 Tips to succeed in the exam.
  • Questions and Answers updated frequently.
  • Ability to study and practice from your mobile device with an intuitive interface.
  • SAA-C01 and SAA-C02 compatible

AWS Certified Solution Architect Associate Prep App Videos Previews:

AWS Certified Solution Architect Associate Prep App Urls:

AWS Solution Architect Associate Training and Certification Preparation App
AWS Solution Architect Associate Training and Certification Preparation App

AWS Certified Solution Architect PRO versions for Ios

AWS Certified Solution Architect PRO versions for Android google

AWS Solutions Architect A. PRO
AWS Solutions Architect A. PRO
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AWS Certified Solution Architect PRO versions for Windows10/11:


AWS Certified Solution Architect PRO versions for Amazon android:

AWS Certified Solution Architect Associate Prep App Content:

Resources section, Various architectural Questions and Answers about AWS, AWS SDK, EBS Volumes, EC2, S3, KMS, AWS read replicas, CloudFront, Elasticity, Virtual Machines, Caching, Containers, Architecture, AWS Security, Lambda, Bastion Hosts, S3 lifecycle policy, kinesis sharing, AWS EBS Volumes, API Gateway, AWS Snapshots, Auto shutdown Ec2 instances, High Availability, RDS, DynamoDB, Elasticity, AWS Virtual Machines, AWS Caching, AWS Containers, AWS Architecture, Load Balancing, EBS, Multi-AZ RDS, Aurora, EFS, NLB, ALB, Aurora, Auto Scaling, DynamoDB(latency), Aurora(performance), Multi-AZ RDS(high availability), Throughput Optimized EBS (highly sequential), CloudWatch, CloudTrail, ElasticBeanstalk, OpsWorks, RPO vs RTO, HA vs FT, Undifferentiated Heavy Lifting, Access Management Basics, Shared Responsibility Model, Cloud Service Models, etc…

The resources sections cover the following areas: Certification, AWS training, Exam Preparation Tips, Cloud Architect Training, Cloud Architecture Knowledge.

Abilities Validated by the AWS Certified Solution Architect Associate Prep App :

  • Effectively demonstrate knowledge of how to architect and deploy secure and robust applications using AWS technologies
  • Define a solution using architectural design principles based on customer requirements
  • Provide implementation guidance based on best practices to the organization throughout the life cycle of the project

AWS Certified Solution Architect Associate Prep App: Exam Preparation Tips:

0

Read FAQs and learn more about the following topics in details: Load Balancing, DynamoDB, EBS, Multi-AZ RDS, Aurora, EFS, DynamoDB, NLB, ALB, Aurora, Auto Scalling, DynamoDB(latency), Aurora(performance), Multi-AZ RDS(high availability), Throughput Optimized EBS (highly sequential), Read the quizlet note cards about Cloudwatch, CloudTrail, KMS, ElasticBeanstalk, OpsWorks here. Read Dexter’s Barely passed AWS Cram Notes about RPO vs RTO, HA vs FT, Undifferentiated Heavy Lifting, Access Management Basics, Shared Responsibility Model, Cloud Service Models
AWS topics for SAA-CO1 and SAA-CO2

1

Know what instance types can be launched from which types of AMIs, and which instance types require an HVM AMIAWS HVM AMI

2

Understand bastion hosts, and which subnet one might live on. Bastion hosts are instances that sit within your public subnet and are typically accessed using SSH or RDP. Once remote connectivity has been established with the bastion host, it then acts as a ‘jump’ server, allowing you to use SSH or RDP to login to other instances (within private subnets) deeper within your network. When properly configured through the use of security groups and Network ACLs, the bastion essentially acts as a bridge to your private instances via the Internet.”
Bastion Hosts

3

Know the difference between Directory Service’s AD Connector and Simple AD. Use Simple AD if you need an inexpensive Active Directory–compatible service with the common directory features. AD Connector lets you simply connect your existing on-premises Active Directory to AWS.
AD Connector and Simple AD

4

Know how to enable cross-account access with IAM: To delegate permission to access a resource, you create an IAM role that has two policies attached. The permissions policy grants the user of the role the needed permissions to carry out the desired tasks on the resource. The trust policy specifies which trusted accounts are allowed to grant its users permissions to assume the role. The trust policy on the role in the trusting account is one-half of the permissions. The other half is a permissions policy attached to the user in the trusted account that allows that user to switch to, or assume the role.
Enable cross-account access with IAM

5

Have a good understanding of how Route53 supports all of the different DNS record types, and when you would use certain ones over others.
Route 53 supports all of the different DNS record types

6

Know which services have native encryption at rest within the region, and which do not.
AWS Services with native Encryption at rest

7

Know which services allow you to retain full admin privileges of the underlying EC2 instances
EC2 Full admin privilege

8

Know When Elastic IPs are free or not: If you associate additional EIPs with that instance, you will be charged for each additional EIP associated with that instance per hour on a pro rata basis. Additional EIPs are only available in Amazon VPC. To ensure efficient use of Elastic IP addresses, we impose a small hourly charge when these IP addresses are not associated with a running instance or when they are associated with a stopped instance or unattached network interface.
When are AWS Elastic IPs Free or not?

9

Know what are the four high level categories of information Trusted Advisor supplies.
#AWS Trusted advisor

10

Know how to troubleshoot a connection time out error when trying to connect to an instance in your VPC. You need a security group rule that allows inbound traffic from your public IP address on the proper port, you need a route that sends all traffic destined outside the VPC (0.0.0.0/0) to the Internet gateway for the VPC, the network ACLs must allow inbound and outbound traffic from your public IP address on the proper port, etc.
#AWS Connection time out error

11

Be able to identify multiple possible use cases and eliminate non-use cases for SWF.
#AWS

12

Understand how you might set up consolidated billing and cross-account access such that individual divisions resources are isolated from each other, but corporate IT can oversee all of it.
#AWS Set up consolidated billing

13

Know how you would go about making changes to an Auto Scaling group, fully understanding what you can and can’t change. “You can only specify one launch configuration for an Auto Scaling group at a time, and you can’t modify a launch configuration after you’ve created it. Therefore, if you want to change the launch configuration for your Auto Scaling group, you must create a launch configuration and then update your Auto Scaling group with the new launch configuration. When you change the launch configuration for your Auto Scaling group, any new instances are launched using the new configuration parameters, but existing instances are not affected.
#AWS Make Change to Auto Scaling group

14

Know how you would go about making changes to an Auto Scaling group, fully understanding what you can and can’t change. “You can only specify one launch configuration for an Auto Scaling group at a time, and you can’t modify a launch configuration after you’ve created it. Therefore, if you want to change the launch configuration for your Auto Scaling group, you must create a launch configuration and then update your Auto Scaling group with the new launch configuration. When you change the launch configuration for your Auto Scaling group, any new instances are launched using the new configuration parameters, but existing instances are not affected.
#AWS Make Change to Auto Scaling group

15

Know which field you use to run a script upon launching your instance.
#AWS User data script

16

Know how DynamoDB (durable, and you can pay for strong consistency), Elasticache (great for speed, not so durable), and S3 (eventual consistency results in lower latency) compare to each other in terms of durability and low latency.
#AWS DynamoDB consistency

17

Know the difference between bucket policies, IAM policies, and ACLs for use with S3, and examples of when you would use each. “With IAM policies, companies can grant IAM users fine-grained control to their Amazon S3 bucket or objects while also retaining full control over everything the users do. With bucket policies, companies can define rules which apply broadly across all requests to their Amazon S3 resources, such as granting write privileges to a subset of Amazon S3 resources. Customers can also restrict access based on an aspect of the request, such as HTTP referrer and IP address. With ACLs, customers can grant specific permissions (i.e. READ, WRITE, FULL_CONTROL) to specific users for an individual bucket or object.
#AWS Difference between bucket policies

18

Know when and how you can encrypt snapshots.
#AWS EBS Encryption

19

Understand how you can use ELB cross-zone load balancing to ensure even distribution of traffic to EC2 instances in multiple AZs registered with a load balancer.
#AWS ELB cross-zone load balancing

20

How would you allow users to log into the AWS console using active directory integration. Here is a link to some good reference material.
#AWS og into the AWS console using active directory integration

21

Spot instances are good for cost optimization, even if it seems you might need to fall back to On-Demand instances if you wind up getting kicked off them and the timeline grows tighter. The primary (but still not only) factor seems to be whether you can gracefully handle instances that die on you–which is pretty much how you should always design everything, anyway!
#AWS Spot instances

22

The term “use case” is not the same as “function” or “capability”. A use case is something that your app/system will need to accomplish, not just behaviour that you will get from that service. In particular, a use case doesn’t require that the service be a 100% turnkey solution for that situation, just that the service plays a valuable role in enabling it.
#AWS use case

23

There might be extra, unnecessary information in some of the questions (red herrings), so try not to get thrown off by them. Understand what services can and can’t do, but don’t ignore “obvious”-but-still-correct answers in favour of super-tricky ones.
#AWS Exam Answers: Distractors

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If you don’t know what they’re trying to ask, in a question, just move on and come back to it later (by using the helpful “mark this question” feature in the exam tool). You could easily spend way more time than you should on a single confusing question if you don’t triage and move on.
#AWS Exa: Skip Questions that are vague and come back to them later

25

Some exam questions required you to understand features and use cases of: VPC peering, cross-account access, DirectConnect, snapshotting EBS RAID arrays, DynamoDB, spot instances, Glacier, AWS/user security responsibilities, etc.
#AWS

26

The 30 Day constraint in the S3 Lifecycle Policy before transitioning to S3-IA and S3-One Zone IA storage classes
#AWS S3 lifecycle policy

27

Enabling Cross-region snapshot copy for an AWS KMS-encrypted cluster
Redis Auth / Amazon MQ / IAM DB Authentication

#AWS Cross-region snapshot copy for an AWS KMS-encrypted cluster

28

Know that FTP is using TCP and not UDP (Helpful for questions where you are asked to troubleshoot the network flow)
TCP and UDP

29

Know the Difference between S3, EBS and EFS
#AWS Difference between S3, EBS and EFS

30

Kinesis Sharding:
#AWS Kinesis Sharding

31

Handling SSL Certificates in ELB ( Wildcard certificate vs SNI )
#AWS Handling SSL Certificates in ELB ( Wildcard certificate vs SNI )

32

Difference between OAI, Signed URL (CloudFront) and Pre-signed URL (S3)
#AWS Difference between OAI, Signed URL (CloudFront) and Pre-signed URL (S3)

33

Different types of Aurora Endpoints
#AWS Different types of Aurora Endpoints

34

The Default Termination Policy for Auto Scaling Group (Oldest launch configuration vs Instance Protection)
#AWS Default Termination Policy for Auto Scaling Group

35

Watch Acloud Guru Videos Lectures while commuting / lunch break – Reschedule the exam if you are not yet ready
#AWS ACloud Guru

36

Watch Linux Academy Videos Lectures while commuting / lunch break – Reschedule the exam if you are not yet ready
#AWS Linux Academy

37

Watch Udemy Videos Lectures while commuting / lunch break – Reschedule the exam if you are not yet ready
#AWS Linux Academy

38

The Udemy practice test interface is good that it pinpoints your weak areas, so what I did was to re-watch all the videos that I got the wrong answers. Since I was able to gauge my exam readiness, I decided to reschedule my exam for 2 more weeks, to help me focus on completing the practice tests.
#AWS Udemy

39

Use AWS Cheatsheets – I also found the cheatsheets provided by Tutorials Dojo very helpful. In my opinion, it is better than Jayendrapatil Patil’s blog since it contains more updated information that complements your review notes.
#AWS Cheat Sheet

40

Watch this exam readiness 3hr video, it very recent webinar this provides what is expected in the exam.
#AWS Exam Prep Video

41

Start off watching Ryan’s videos. Try and completely focus on the hands on. Take your time to understand what you are trying to learn and achieve in those LAB Sessions.
#AWS Exam Prep Video

42

Do not rush into completing the videos. Take your time and hone the basics. Focus and spend a lot of time for the back bone of AWS infrastructure – Compute/EC2 section, Storage (S3/EBS/EFS), Networking (Route 53/Load Balancers), RDS, VPC, Route 3. These sections are vast, with lot of concepts to go over and have loads to learn. Trust me you will need to thoroughly understand each one of them to ensure you pass the certification comfortably.
#AWS Exam Prep Video

43

Make sure you go through resources section and also AWS documentation for each components. Go over FAQs. If you have a question, please post it in the community. Trust me, each answer here helps you understand more about AWS.
#AWS Faqs

44

Like any other product/service, each AWS offering has a different flavor. I will take an example of EC2 (Spot/Reserved/Dedicated/On Demand etc.). Make sure you understand what they are, what are the pros/cons of each of these flavors. Applies for all other offerings too.
#AWS Services

45

Ensure to attend all quizzes after each section. Please do not treat these quizzes as your practice exams. These quizzes are designed to mostly test your knowledge on the section you just finished. The exam itself is designed to test you with scenarios and questions, where in you will need to recall and apply your knowledge of different AWS technologies/services you learn over multiple lectures.
#AWS Services

46

I, personally, do not recommend to attempt a practice exam or simulator exam until you have done all of the above. It was a little overwhelming for me. I had thoroughly gone over the videos. And understood the concepts pretty well, but once I opened exam simulator I felt the questions were pretty difficult. I also had a feeling that videos do not cover lot of topics. But later I realized, given the vastness of AWS Services and offerings it is really difficult to encompass all these services and their details in the course content. The fact that these services keep changing so often, does not help
#AWS Services

47

Go back and make a note of all topics, that you felt were unfamiliar for you. Go through the resources section and fiund links to AWS documentation. After going over them, you shoud gain at least 5-10% more knowledge on AWS. Have expectations from the online courses as a way to get thorough understanding of basics and strong foundations for your AWS knowledge. But once you are done with videos. Make sure you spend a lot of time on AWS documentation and FAQs. There are many many topics/sub topics which may not be covered in the course and you would need to know, atleast their basic functionalities, to do well in the exam.
#AWS Services

48

Once you start taking practice exams, it may seem really difficult at the beginning. So, please do not panic if you find the questions complicated or difficult. IMO they are designed or put in a way to sound complicated but they are not. Be calm and read questions very carefully. In my observation, many questions have lot of information which sometimes is not relevant to the solution you are expected to provide. Read the question slowly and read it again until you understand what is expected out of it.
#AWS Services

49

With each practice exam you will come across topics that you may need to scale your knowledge on or learn them from scratch.
#AWS Services

50

With each test and the subsequent revision, you will surely feel more confident.
There are 130 mins for questions. 2 mins for each question which is plenty of time.
At least take 8-10 practice tests. The ones on udemy/tutorialdojo are really good. If you are a acloudguru member. The exam simulator is really good.
Manage your time well. Keep patience. I saw someone mention in one of the discussions that do not under estimate the mental focus/strength needed to sit through 130 mins solving these questions. And it is really true.
Do not give away or waste any of those precious 130 mins. While answering flag/mark questions you think you are not completely sure. My advice is, even if you finish early, spend your time reviewing the answers. I could review 40 of my answers at the end of test. And I at least rectified 3 of them (which is 4-5% of total score, I think)
So in short – Put a lot of focus on making your foundations strong. Make sure you go through AWS Documentation and FAQs. Try and envision how all of the AWS components can fit together and provide an optimal solution. Keep calm.
This video gives outline about exam, must watch before or after Ryan’s course. #AWS Services

51

Walking you through how to best prepare for the AWS Certified Solutions Architect Associate SAA-C02 exam in 5 steps:
1. Understand the exam blueprint
2. Learn about the new topics included in the SAA-C02 version of the exam
3. Use the many FREE resources available to gain and deepen your knowledge
4. Enroll in our hands-on video course to learn AWS in depth
5. Use practice tests to fully prepare yourself for the exam and assess your exam readiness
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

52

Storage:
1. Know your different Amazon S3 storage tiers! You need to know the use cases, features and limitations, and relative costs; e.g. retrieval costs.
2. Amazon S3 lifecycle policies is also required knowledge — there are minimum storage times in certain tiers that you need to know.
3. For Glacier, you need to understand what it is, what it’s used for, and what the options are for retrieval times and fees.
4. For the Amazon Elastic File System (EFS), make sure you’re clear which operating systems you can use with it (just Linux).
5. For the Amazon Elastic Block Store (EBS), make sure you know when to use the different tiers including instance stores; e.g. what would you use for a datastore that requires the highest IO and the data is distributed across multiple instances? (Good instance store use case)
6. Learn about Amazon FSx. You’ll need to know about FSx for Windows and Lustre.
7. Know how to improve Amazon S3 performance including using CloudFront, and byte-range fetches — check out this whitepaper.
8. Make sure you understand about Amazon S3 object deletion protection options including versioning and MFA delete.
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

53

Compute:
1. You need to have a good understanding of the options for how to scale an Auto Scaling Group using metrics such as SQS queue depth, or numbers of SNS messages.
2. Know your different Auto Scaling policies including Target Tracking Policies.
3. Read up on High Performance Computing (HPC) with AWS. You’ll need to know about Amazon FSx with HPC use cases.
4. Know your placement groups. Make sure you can differentiate between spread, cluster and partition; e.g. what would you use for lowest latency? What about if you need to support an app that’s tightly coupled? Within an AZ or cross AZ?
5. Make sure you know the difference between Elastic Network Adapters (ENAs), Elastic Network Interfaces (ENIs) and Elastic Fabric Adapters (EFAs).
6. For the Amazon Elastic Container Service (ECS), make sure you understand how to assign IAM policies to ECS for providing S3 access. How can you decouple an ECS data processing process — Kinesis Firehose or SQS?
7. Make sure you’re clear on the different EC2 pricing models including Reserved Instances (RI) and the different RI options such as scheduled RIs.
8. Make sure you know the maximum execution time for AWS Lambda (it’s currently 900 seconds or 15 minutes).
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

54

Network
1. Understand what AWS Global Accelerator is and its use cases.
2. Understand when to use CloudFront and when to use AWS Global Accelerator.
3. Make sure you understand the different types of VPC endpoint and which require an Elastic Network Interface (ENI) and which require a route table entry.
4. You need to know how to connect multiple accounts; e.g. should you use VPC peering or a VPC endpoint?
5. Know the difference between PrivateLink and ClassicLink.
6. Know the patterns for extending a secure on-premises environment into AWS.
7. Know how to encrypt AWS Direct Connect (you can use a Virtual Private Gateway / AWS VPN).
8. Understand when to use Direct Connect vs Snowball to migrate data — lead time can be an issue with Direct Connect if you’re in a hurry.
9. Know how to prevent circumvention of Amazon CloudFront; e.g. Origin Access Identity (OAI) or signed URLs / signed cookies.
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

55

Databases
1. Make sure you understand Amazon Aurora and Amazon Aurora Serverless.
2. Know which RDS databases can have Read Replicas and whether you can read from a Multi-AZ standby.
3. Know the options for encrypting an existing RDS database; e.g. only at creation time otherwise you must encrypt a snapshot and create a new instance from the snapshot.
4. Know which databases are key-value stores; e.g. Amazon DynamoDB.
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

56

Application Integration
1. Make sure you know the use cases for the Amazon Simple Queue Service (SQS), and Simple Notification Service (SNS).
2. Understand the differences between Amazon Kinesis Firehose and SQS and when you would use each service.
3. Know how to use Amazon S3 event notifications to publish events to SQS — here’s a good “How To” article.
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

57

Management and Governance
1. You’ll need to know about AWS Organizations; e.g. how to migrate an account between organizations.
2. For AWS Organizations, you also need to know how to restrict actions using service control policies attached to OUs.
3. Understand what AWS Resource Access Manager is.
AWS CERTIFIED SOLUTIONS ARCHITECT SAA-C02 : HOW TO BEST PREPARE IN 5 STEPS

58

Jon Bonso list of helpful exam prep materials that you can use.
1. The official AWS SAA-C02 Certification Exam page.
2. The official AWS Exam Guide.
3. The official AWS Sample Questions
4. The official AWS Ramp-Up Guide: Architect PDF
5. Tutorials Dojo SAA-C02 Study Guide
6. Udemy Practice Exams
7. New AWS Services to prepare for:AWS Global Accelerator
8. New AWS Services to prepare for: Elastic Fabric Adapter — Amazon Web Services
9. New AWS Services to prepare for: AWS ParallelCluster – Amazon Web Services
10. New AWS Services to prepare for: Amazon FSx File Storage
Pass your SAA-C02 (AWS Solutions Architect Associate) exam with these Top 5 Resources

Other AWS Certified Solution Architect Associate Prep App

AWS Certified Solution Architect Associate Prep App: Additional Information for reference

Get the AWS SAA SAA-C02 SAA-C03 Exam Prep App oniOS – AndroidWindows 10/11

Below are some useful reference links that would help you to learn about AWS Practitioner Exam.

AWS Certified Solution Architect Associate Prep: Whitepapers:

AWS has provided whitepapers to help you understand the technical concepts. Below are the recommended whitepapers.

Note and disclaimer: We are not affiliated with AWS or Amazon or Microsoft or Google. The questions are put together based on the certification study guide and materials available online. We also receive questions and answers from anonymous users and we vet to make sure they are legitimate. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

AWS Solution Architect Associate Training and Certification Preparation App
AWS Solution Architect Associate Training and Certification Preparation App

Get the AWS SAA SAA-C02 SAA-C03 Exam Prep App oniOS – AndroidWindows 10/11


AWS Certified Cloud Practitioner Exam Prep App

AWS Certified Cloud Practitioner CLF-C01 Training and Certification Prep

AWS CCP clf-c01 certified cloud practitioner exam prep PRO
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The AWS Certified Cloud Practitioner average salary is — $131,465/year

What is the AWS Certified Cloud Practitioner Exam?

The AWS Certified Cloud Practitioner Exam (CLF-C01) is an introduction to AWS services and the intention is to examine the candidates ability to define what the AWS cloud is and its global infrastructure. It provides an overview of AWS core services security aspects, pricing and support services. The main objective is to provide an overall understanding about the Amazon Web Services Cloud platform. The course helps you get the conceptual understanding of the AWS and can help you know about the basics of AWS and cloud computing, including the services, cases and benefits.


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To succeed with the real exam, do not memorize the answers below. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

This AWS Certified Cloud Practitioner Exam Prep App (CCP, CLF-C01) helps you prepare and train for the AWS Certified Cloud Practitioner Exam with mock exams and various questions and answers.
You can use the AWS Certified Cloud Practitioner Exam Prep App to study anytime, anywhere from your phone, tablet, computer.

AWS Certified Cloud Practitioner Exam Prep App Features:


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  • 3 Mock exams
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    The questions and Answers are divided in 4 categories: Technology, Security and Compliance, Cloud Concepts, Billing and Pricing.


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AWS Certified Cloud Practitioner Exam Prep App Videos Preview:

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AWS Certified Cloud Practitioner PRO Versions for Windows 10/Microsoft:

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AWS Certified Cloud Practitioner Exam Prep App Benefits:

After successfully taking all mock exams and quizzes in this app, you should be able to:

  • Explain the value of the AWS Cloud.
  • Understand and explain the AWS shared responsibility model.
  • Understand AWS Cloud security best practices.
  • Understand AWS Cloud costs, economics, and billing practices.
  • Describe and position the core AWS services, including compute, network, databases, and storage.
  • Identify AWS services for common use cases.

Abilities Validated by the Certification using theAWS Certified Cloud Practitioner Exam Prep App :

  • Define what the AWS Cloud is and the basic global infrastructure
  • Describe basic AWS Cloud architectural principles
  • Describe the AWS Cloud value proposition
  • Describe key services on the AWS platform and their common use cases
  • Describe basic security and compliance aspects of the AWS platform and the shared security model
  • Define the billing, account management, and pricing models
  • Identify sources of documentation or technical assistance
  • Describe basic/core characteristics of deploying and operating in the AWS Cloud

After successfully taking all mock exams and quizzes in this app, you should be able to:

  • Explain the value of the AWS Cloud.
  • Understand and explain the AWS shared responsibility model.
  • Understand AWS Cloud security best practices.
  • Understand AWS Cloud costs, economics, and billing practices.
  • Describe and position the core AWS services, including compute, network, databases, and storage.
  • Identify AWS services for common use cases.

Note and disclaimer: We are not affiliated with AWS or Amazon or Microsoft or Google. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

AWS Certified Cloud Practitioner Exam Prep App Content:

The quizzes and mock exams cover the following topics: VPC, S3, DynamoDB, EC2, ECS, Lambda, API Gateway, CloudWatch, CloudTrail, Code Pipeline, Code Deploy, TCO Calculator, SES, EBS, ELB, AWS Autoscaling , RDS, Aurora, Route 53, Amazon CodeGuru, Amazon Bracket, AWS Billing and Pricing, Simply Monthly Calculator, cost calculator, Ec2 pricing on-demand, AWS Pricing, Pay As You Go, No Upfront Cost, Cost Explorer, AWS Organizations, Consolidated billing, Instance Scheduler, on-demand instances, Reserved instances, Spot Instances, CloudFront, Web hosting on S3, S3 storage classes, Regions, Availability Zones,

The resources sections cover the following areas: AWS Certification, AWS training, Cloud Technology, CCP new version, cloud certification, cloud exam preparation tips, cloud practitioner exam questions, amazon cloud practitioner, amazon cloud practitioner exam questions, certification dumps, google cloud, azure cloud, cloud comparison, CLF-C01, cloud practitioner exam, aws certified cloud practitioner study guide, aws certified cloud practitioner white papers, aws whitepapers cloud practitioner, cloud practitioner exam guide, aws cloud practitioner exam review, aws cloud practitioner preparation, aws cloud practitioner study guide, aws cloud practitioner jobs, aws certified cloud practitioner jobs.

AWS Certified Cloud Practitioner Exam Whitepapers:

AWS has provided whitepapers to help you understand the technical concepts. Below are the recommended whitepapers.

Online Training and Labs for AWS Cloud Certified Practitioner Exam

Additional Information for reference

Below are some useful reference links that would help you to learn about AWS Practitioner Exam.

AWS Certified Cloud Practitioner Exam Prep App Resources

1-AWS Route 53
Route 53 is a domain name system service by AWS. When a Disaster does occur , it can be easy to switch to secondary sites using the Route53 service. Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service. It is designed to give developers and businesses an extremely reliable and cost effective way to route end users to Internet applications by translating names like www.example.com into the numeric IP addresses like 192.0.2.1 that computers use to connect to each other. Amazon Route 53 is fully compliant with IPv6 as well.-

AWS CloudWatch

CloudWatch is used to collect, view, and track metrics for resources (such as EC2 instances) in your AWS account.

AWS Elasticache
ElastiCache is a web service that makes it easy to set up, manage, and scale a distributed in-memory data store or cache environment in the cloud. It provides a high-performance, scalable, and cost-effective caching solution, while removing the complexity associated with deploying and managing a distributed cache environment. Redis and Memcached are popular, open-source, in-memory data stores. Although they are both easy to use and offer high performance, there are important differences to consider when choosing an engine. Memcached is designed for simplicity while Redis offers a rich set of features that make it effective for a wide range of use cases. Understand your requirements and what each engine offers to decide which solution better meets your needs

Difference between RDS and DynamoDB
RDS is a SQL database service (that offers several database engine options), and DynamoDB is a NoSQL database option that only offers one NoSQL engine

High Availability
High availability refers to the concept that something will be accessible when you try to access it. An object or web application is “highly available” when it is accessible a vast majority of the time.

Cost optimization, Automating, Elasticity
Elasticity (think of a rubber band) defines a system that can easily (and cost-effectively) grow and shrink based on required demand.

Designing fault tolerant applications
Fault tolerance describes the concept of a system (in our case a web application) to have failure in some of its components and still remain accessible (highly available). Fault tolerant web applications will have at least two web servers (in case one fails).

AWS
AWS is defined as a cloud services provider. They provide hundreds of services of which compute and storage are included (not not limited to).

AWS s3 and AWS EBS
Amazon S3 is a Object storage built to store and retrieve any amount of data from anywhere. Amazon Elastic Block Store is a Persistent block storage for Amazon EC2

AWS EC2
AWS EC2 can be used to host virtual servers on AWS.

Uploading an archive in AWS
The AWS Console cannot be used to upload data onto Glacier. The console can only be used to create a Glacier vault which can be used to upload the data.

AWS Ec2
If you want a self-managed database, that means you want complete control over the database engine and the underlying infrastructure. In such a case you need to host the database on an EC2 Instance.

AWS tools
AWS SDK can be plugged in for various programming languages. Using the SDK you can then call the required AWS services.

AWS EBS Volumes
When you create an EBS volume in an Availability Zone, it is automatically replicated within that zone to prevent data loss due to failure of any single hardware component.

AWS read replicas
You can reduce the load on your source DB Instance by routing read queries from your applications to the read replica. Read replicas allow you to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads.

AWS EC2 Spot Instances
When you think of cost effectiveness, you can either have to choose Spot or Reserved instances. Now when you have a regular processing job, the best is to use spot instances and since your application is designed recover gracefully from Amazon EC2 instance failures, then even if you lose the Spot instance , there is no issue because your application can recover.

AWS Elasticache
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases.

AWS Disaster Recovery
The following figure shows a spectrum for the four scenarios, arranged by how quickly a system can be available to users after a DR event: Backup & Restore -> Pilot Light -> Warm Standby -> Multi SIte

AWS DynamoDB
DynamoDB does not use/support other NoSQL database engines. You only have access to use DynamoDB’s built-in engine.

AWS Redshift
Redshift is a database offering that is fully-managed and used for data warehousing and analytics, including compatibility with existing business intelligence tools.

AWS S3 Storage Classes
S3 Standard Storage class has a rating of 99.999999999% durability (referred to as 11 nines) and 99.99% availability.

AWS storage-classes
The Standard storage class should be used for files that you access on a daily or very frequent basis.

AWS SQS
Amazon Simple Queue Service (Amazon SQS) offers a reliable, highly-scalable hosted queue for storing messages as they travel between applications or microservices. It moves data between distributed application components and helps you decouple these components.

AWS Reserved Instances
Reserved instances are the best choice for instances with continuous usage and offer a reduced cost because you purchase the instance for the entire year.Amazon EC2 Reserved Instances (RI) provide a significant discount (up to 75%) compared to On-Demand pricing and provide a capacity reservation when used in a specific Availability Zone.

AWS CloudFront
Lambda@Edge lets you run Lambda functions to customize the content that CloudFront delivers, executing the functions in AWS locations closer to the viewer.
Amazon CloudFront is a web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users. CloudFront delivers your content through a worldwide network of data centers called edge locations. When a user requests content that you’re serving with CloudFront, the user is routed to the edge location that provides the lowest latency (time delay), so that content is delivered with the best possible performance.
CloudFront speeds up the distribution of your content by routing each user request through the AWS backbone network to the edge location that can best serve your content. Typically, this is a CloudFront edge server that provides the fastest delivery to the viewer. Using the AWS network dramatically reduces the number of networks that your users’ requests must pass through, which improves performance. Users get lower latency—the time it takes to load the first byte of the file—and higher data transfer rates.
You also get increased reliability and availability because copies of your files (also known as objects) are now held (or cached) in multiple edge locations around the world.

AWS EC2 Instance info and details
How to get information about Ec2 instance type?

What load balancing options does the Elastic Load Balancing service offer?
Elastic Load Balancing offers two types of load balancers that both feature high availability, automatic scaling, and robust security. These include the Classic Load Balancer that routes traffic based on either application or network level information, and the Application Load Balancer that routes traffic based on advanced application level information that includes the content of the request.

How many instances can I run in Amazon EC2?
You are limited to running up to a total of 20 On-Demand instances across the instance family, purchasing 20 Reserved Instances, and requesting Spot Instances per your dynamic Spot limit per region.

AWS Elasticache
Redis, MemcacheD

CloudWatch
CloudWatch is used to collect, view, and track metrics for resources (such as EC2 instances) in your AWS account.

Edge Locations
With Lambda@Edge you can easily run your code across AWS locations globally, allowing you to respond to your end users at the lowest latency and allowing you to personalize content.

AWS Certified Cloud Practitioner CLF-C01 Training and Certification Prep
AWS Certified Cloud Practitioner CLF-C01 Training and Certification Prep


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AWS Developer Certification Exam Prep App

aws certified developer associate

This blog is about the AWS Developer Certification Exam Prep App: Developer Associate Exam Prep, Mock Exams, Quizzes, Tips to succeed in the AWS Developer Certification Exam.

This AWS Cloud Training App provides tools and features essentials to prepare and succeed in the AWS Certified Developer Associate Exam:

  • 2 Mock Exams,
  • 4 Quizzes (30+ Questions per Quiz),
  • Score card for quizzes and mock exams
  • Score Tracker,
  • Detailed Answers and Reference for each Question
  • Countdown timer,
  • Questions and Answers for Development With AWS, Deployment, Monitoring, Troubleshooting, Refactoring.

AWS Developer Certification Exam Prep App Preview Videos:

The AWS Developer Certification Exam Prep App is available for all platforms and in most app stores worldwide:

AWS Developer Associates PRO version with mock exam iOs:

AWS Developer Associates PRO version with mock exam android

2022 AWS Developer Assoc. PRO
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AWS Developer Associates PRO at Huawei App Gallery

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AWS Developer Associates Free version PWA/Web:

AWS Developer Associates Free version at Google Play Store (android):

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AWS Developer Associates Free version at Microsoft/Windows10 App store:

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AWS Developer Certification Exam Prep App Content:

The App covers the following topics:

  • Use AWS Cheatsheets – I also found the cheatsheets provided by Tutorials Dojo very helpful. In my opinion, it is better than Jayendrapatil Patil’s blog since it contains more updated information that complements your review notes.
    #AWS Cheat Sheet
  • Do not rush into completing the videos. Take your time and hone the basics. Focus and spend a lot of time for the back bone of AWS infrastructure – Compute/EC2 section, Storage (S3/EBS/EFS), Networking (Route 53/Load Balancers), RDS, VPC, Route 3. These sections are vast, with lot of concepts to go over and have loads to learn. Trust me you will need to thoroughly understand each one of them to ensure you pass the certification comfortably.
    #AWS Exam Prep Video
  • Make sure you go through resources section and also AWS documentation for each components. Go over FAQs. If you have a question, please post it in the community. Trust me, each answer here helps you understand more about AWS.
    #AWS Faqs
  • Like any other product/service, each AWS offering has a different flavor. I will take an example of EC2 (Spot/Reserved/Dedicated/On Demand etc.). Make sure you understand what they are, what are the pros/cons of each of these flavors. Applies for all other offerings too.
    #AWS Services

What is the AWS Certified Developer Associate Exam?

The AWS Certified Developer – Associate examination is intended for individuals who perform a development role and have one or more years of hands-on experience developing and maintaining an AWS-based application. It validates an examinee’s ability to:
Demonstrate an understanding of core AWS services, uses, and basic AWS architecture best practicesDemonstrate proficiency in developing, deploying, and debugging cloud-based applications using AWS

Other AWS Developer Certification Exam Preparation Resources:

Additional Information for reference

Below are some useful reference links that would help you to learn about AWS Practitioner Exam.

AWS Developer Associate Exam Whitepapers:

AWS has provided whitepapers to help you understand the technical concepts. Below are the recommended whitepapers.

Online Training and Labs for AWS Certified Developer Associate Exam

AWS Certified Developer Associate Jobs

Note and disclaimer: We are not affiliated with AWS or Amazon or Microsoft or Google. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.