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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.
The App provides hundreds of quizzes and practice exam about:
– Machine Learning Operation on AWS
– Data Engineering
– Computer Vision,
– Exploratory Data Analysis,
– ML implementation & Operations
– Machine Learning Basics Questions and Answers
– Machine Learning Advanced Questions and Answers
– 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
Recommend and implement the appropriate machine learning services and features for a given
Apply basic AWS security practices to machine learning solutions.
Deploy and operationalize machine learning solutions.
Machine Learning Services covered:
AWS Deep Learning AMIs (DLAMI)
Amazon Fraud Detector
Other Services and topics covered are:
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.
- Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom modelsby Goktug Cinar (AWS Machine Learning Blog) on September 23, 2022 at 4:54 pm
This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both time consuming and subjective. Such manual
- Detect population variance of endangered species using Amazon Rekognitionby Jyothi Goudar (AWS Machine Learning Blog) on September 22, 2022 at 9:25 pm
Our planet faces a global extinction crisis. UN Report shows a staggering number of more than a million species feared to be on the path of extinction. The most common reasons for extinction include loss of habitat, poaching, and invasive species. Several wildlife conservation foundations, research scientists, volunteers, and anti-poaching rangers have been working tirelessly
- How Amazon Search reduced ML inference costs by 85% with AWS Inferentiaby Joao Moura (AWS Machine Learning Blog) on September 22, 2022 at 6:12 pm
Amazon’s product search engine indexes billions of products, serves hundreds of millions of customers worldwide, and is one of the most heavily used services in the world. The Amazon Search team develops machine learning (ML) technology that powers the Amazon.com search engine and helps customers search effortlessly. To deliver a great customer experience and operate
- Amazon Comprehend Targeted Sentiment adds synchronous supportby Raj Pathak (AWS Machine Learning Blog) on September 21, 2022 at 9:27 pm
Earlier this year, Amazon Comprehend, a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text, launched the Targeted Sentiment feature. With Targeted Sentiment, you can identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and offer
- Run machine learning enablement events at scale using AWS DeepRacer multi-user account modeby Marius Cealera (AWS Machine Learning Blog) on September 21, 2022 at 4:19 pm
This post was co-written by Marius Cealera, Senior Partner Solutions Architect at AWS, Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, Cloud Architect at Accenture. Machine learning (ML) is a high-stakes business priority, with companies spending $306 billion on ML applications in the past 3 years. According to Accenture, companies that scale ML across
- Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSightby Aleksandr Patrushev (AWS Machine Learning Blog) on September 21, 2022 at 4:15 pm
Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success. A very prominent part of the
- Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluonby Janisha Anand (AWS Machine Learning Blog) on September 21, 2022 at 3:04 pm
Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than
- Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wranglerby Meenakshisundaram Thandavarayan (AWS Machine Learning Blog) on September 20, 2022 at 10:41 pm
Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of
- Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industryby Rohit Banga (AWS Machine Learning Blog) on September 20, 2022 at 5:46 pm
Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and
- Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabularby Xin Huang (AWS Machine Learning Blog) on September 20, 2022 at 5:39 pm
Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. These algorithms and models can be used for both supervised and unsupervised learning. They can process various types of input data, including tabular,
- Parallel data processing with RStudio on Amazon SageMakerby Raj Pathak (AWS Machine Learning Blog) on September 19, 2022 at 4:39 pm
Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE, and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML)
- Discover insights from Zendesk with Amazon Kendra intelligent searchby Rajesh Kumar Ravi (AWS Machine Learning Blog) on September 16, 2022 at 7:02 pm
Customer relationship management (CRM) is a critical tool that organizations maintain to manage customer interactions and build business relationships. Zendesk is a CRM tool that makes it easy for customers and businesses to keep in sync. Zendesk captures a wealth of customer data, such as support tickets created and updated by customers and service agents,
- Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperbandby Doug Mbaya (AWS Machine Learning Blog) on September 16, 2022 at 4:42 pm
Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model. In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast. The benefits of Hyperband Hyperband presents two advantages over
- Read webpages and highlight content using Amazon Pollyby Mike Havey (AWS Machine Learning Blog) on September 16, 2022 at 3:23 pm
In this post, we demonstrate how to use Amazon Polly—a leading cloud service that converts text into lifelike speech—to read the content of a webpage and highlight the content as it’s being read. Adding audio playback to a webpage improves the accessibility and visitor experience of the page. Audio-enhanced content is more impactful and memorable,
- Use Amazon SageMaker Data Wrangler for data preparation and Studio Labs to learn and experiment with MLby Rajakumar Sampathkumar (AWS Machine Learning Blog) on September 15, 2022 at 4:14 pm
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML
- Announcing Visual Conversation Builder for Amazon Lexby Thomas Rindfuss (AWS Machine Learning Blog) on September 14, 2022 at 8:38 pm
Amazon Lex is a service for building conversational interfaces using voice and text. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, natural language bots to new and existing applications. Amazon Lex reduces multi-platform development efforts, allowing you to easily publish your speech or text chatbots to
- Get better insight from reviews using Amazon Comprehendby Rushdi Shams (AWS Machine Learning Blog) on September 13, 2022 at 4:19 pm
“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for
- Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessionsby Sean Morgan (AWS Machine Learning Blog) on September 13, 2022 at 4:01 pm
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and
- Save the date: Join AWS at NVIDIA GTC, September 19–22by Jeremy Singh (AWS Machine Learning Blog) on September 12, 2022 at 6:41 pm
Register free for NVIDIA GTC to learn from experts on how AI and the evolution of the 3D internet are profoundly impacting industries—and society as a whole. We have prepared several AWS sessions to give you guidance on how to use AWS services powered by NVIDIA technology to meet your goals. Amazon Elastic Compute Cloud
- How Medidata used Amazon SageMaker asynchronous inference to accelerate ML inference predictions up to 30 times fasterby Rajnish Jain (AWS Machine Learning Blog) on September 12, 2022 at 6:36 pm
This post is co-written with Rajnish Jain, Priyanka Kulkarni and Daniel Johnson from Medidata. Medidata is leading the digital transformation of life sciences, creating hope for millions of patients. Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical devices, and diagnostics companies as well as academic researchers with accelerating value, minimizing risk,
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