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
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Download AWS machine Learning Specialty Exam Prep App on iOs
Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon
Elevate Your Career with AI & Machine Learning For Dummies PRO
Ready to accelerate your career in the fast-growing fields of AI and machine learning? Our app offers user-friendly tutorials and interactive exercises designed to boost your skills and make you stand out to employers. Whether you're aiming for a promotion or searching for a better job, AI & Machine Learning For Dummies PRO is your gateway to success. Start mastering the technologies shaping the future—download now and take the next step in your professional journey!
Download the AI & Machine Learning For Dummies PRO App:
iOS - Android
Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:
- AWS Certified AI Practitioner (AIF-C01): Conquer the AWS Certified AI Practitioner exam with our AI and Machine Learning For Dummies test prep. Master fundamental AI concepts, AWS AI services, and ethical considerations.
- Azure AI Fundamentals: Ace the Azure AI Fundamentals exam with our comprehensive test prep. Learn the basics of AI, Azure AI services, and their applications.
- Google Cloud Professional Machine Learning Engineer: Nail the Google Professional Machine Learning Engineer exam with our expert-designed test prep. Deepen your understanding of ML algorithms, models, and deployment strategies.
- AWS Certified Machine Learning Specialty: Dominate the AWS Certified Machine Learning Specialty exam with our targeted test prep. Master advanced ML techniques, AWS ML services, and practical applications.
- AWS Certified Data Engineer Associate (DEA-C01): Set yourself up for promotion, get a better job or Increase your salary by Acing the AWS DEA-C01 Certification.
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
- Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWSby Cassandre Vandeputte (AWS Machine Learning Blog) on April 29, 2025 at 4:32 pm
In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.
- InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AIby Vu Le (AWS Machine Learning Blog) on April 29, 2025 at 4:21 pm
This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.
- Improve Amazon Nova migration performance with data-aware prompt optimizationby Yunfei Bai (AWS Machine Learning Blog) on April 29, 2025 at 4:18 pm
In this post, we present an LLM migration paradigm and architecture, including a continuous process of model evaluation, prompt generation using Amazon Bedrock, and data-aware optimization. The solution evaluates the model performance before migration and iteratively optimizes the Amazon Nova model prompts using user-provided dataset and objective metrics.
- Customize Amazon Nova models to improve tool usageby Baishali Chaudhury (AWS Machine Learning Blog) on April 28, 2025 at 5:47 pm
In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we explain in detail the fine-tuning process, hosting fine-tuned models with provisioned throughput, and using the fine-tuned Amazon Nova models for inference.
- Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judgeby Rishiraj Chandra (AWS Machine Learning Blog) on April 28, 2025 at 3:31 pm
In this post, we introduced the Open Source Bedrock Agent Evaluation framework, a Langfuse-integrated solution that streamlines the agent development process. We demonstrated how this evaluation framework can be integrated with pharmaceutical research agents. We used it to evaluate agent performance against biomarker questions and sent traces to Langfuse to view evaluation metrics across question types.
- Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scaleby Renuka Kumar, Toby Fotherby, Shweta Keshavanarayana, Thomas Matthew, Daniel Vaquero, Atul Varshneya, and Jessica Wu (AWS Machine Learning Blog) on April 24, 2025 at 4:23 pm
In this post, the AWS and Cisco teams unveil a new methodical approach that addresses the challenges of enterprise-grade SQL generation. The teams were able to reduce the complexity of the NL2SQL process while delivering higher accuracy and better overall performance.
- AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation modelby Anuj Jauhari (AWS Machine Learning Blog) on April 24, 2025 at 4:17 pm
The AFX team’s product migration to the Nova Lite model has delivered tangible enterprise value by enhancing sales workflows. By migrating to the Amazon Nova Lite model, the team has not only achieved significant cost savings and reduced latency, but has also empowered sellers with a leading intelligent and reliable solution.
- Combine keyword and semantic search for text and images using Amazon Bedrock and Amazon OpenSearch Serviceby Renan Bertolazzi (AWS Machine Learning Blog) on April 24, 2025 at 4:13 pm
In this post, we walk you through how to build a hybrid search solution using OpenSearch Service powered by multimodal embeddings from the Amazon Titan Multimodal Embeddings G1 model through Amazon Bedrock. This solution demonstrates how you can enable users to submit both text and images as queries to retrieve relevant results from a sample retail image dataset.
- Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMakerby Nick Biso (AWS Machine Learning Blog) on April 23, 2025 at 4:06 pm
In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.
- Protect sensitive data in RAG applications with Amazon Bedrockby Praveen Chamarthi (AWS Machine Learning Blog) on April 23, 2025 at 4:00 pm
In this post, we explore two approaches for securing sensitive data in RAG applications using Amazon Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an Amazon Bedrock knowledge base, and the second demonstrated a fine-grained RBAC pattern for managing access to sensitive information during retrieval. These solutions represent just two possible approaches among many for securing sensitive data in generative AI applications.
- Supercharge your LLM performance with Amazon SageMaker Large Model Inference container v15by Vivek Gangasani (AWS Machine Learning Blog) on April 22, 2025 at 5:28 pm
Today, we’re excited to announce the launch of Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4 with support for the vLLM V1 engine. This release introduces significant performance improvements, expanded model compatibility with multimodality (that is, the ability to understand and analyze text-to-text, images-to-text, and text-to-images data), and provides built-in integration with vLLM to help you seamlessly deploy and serve large language models (LLMs) with the highest performance at scale.
- Accuracy evaluation framework for Amazon Q Business – Part 2by Rui Cardoso (AWS Machine Learning Blog) on April 22, 2025 at 5:18 pm
In the first post of this series, we introduced a comprehensive evaluation framework for Amazon Q Business, a fully managed Retrieval Augmented Generation (RAG) solution that uses your company’s proprietary data without the complexity of managing large language models (LLMs). The first post focused on selecting appropriate use cases, preparing data, and implementing metrics to
- Use Amazon Bedrock Intelligent Prompt Routing for cost and latency benefitsby Shreyas Subramanian (AWS Machine Learning Blog) on April 22, 2025 at 5:15 pm
Today, we’re happy to announce the general availability of Amazon Bedrock Intelligent Prompt Routing. In this blog post, we detail various highlights from our internal testing, how you can get started, and point out some caveats and best practices. We encourage you to incorporate Amazon Bedrock Intelligent Prompt Routing into your new and existing generative AI applications.
- How Infosys improved accessibility for Event Knowledge using Amazon Nova Pro, Amazon Bedrock and Amazon Elemental Media Servicesby Aparajithan Vaidyanathan (AWS Machine Learning Blog) on April 22, 2025 at 5:12 pm
In this post, we explore how Infosys developed Infosys Event AI to unlock the insights generated from events and conferences. Through its suite of features—including real-time transcription, intelligent summaries, and an interactive chat assistant—Infosys Event AI makes event knowledge accessible and provides an immersive engagement solution for the attendees, during and after the event.
- Amazon Bedrock Prompt Optimization Drives LLM Applications Innovation for Yuewen Groupby Wang Rui (AWS Machine Learning Blog) on April 21, 2025 at 10:57 pm
Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this blog post, we discuss how Prompt Optimization improves the performance of large language models (LLMs) for intelligent text processing task in Yuewen Group.
- Build a location-aware agent using Amazon Bedrock Agents and Foursquare APIsby John Baker (AWS Machine Learning Blog) on April 21, 2025 at 6:45 pm
In this post, we combine Amazon Bedrock Agents and Foursquare APIs to demonstrate how you can use a location-aware agent to bring personalized responses to your users.
- Build an automated generative AI solution evaluation pipeline with Amazon Novaby Deepak Dalakoti (AWS Machine Learning Blog) on April 21, 2025 at 5:16 pm
In this post, we explore the importance of evaluating LLMs in the context of generative AI applications, highlighting the challenges posed by issues like hallucinations and biases. We introduced a comprehensive solution using AWS services to automate the evaluation process, allowing for continuous monitoring and assessment of LLM performance. By using tools like the FMeval Library, Ragas, LLMeter, and Step Functions, the solution provides flexibility and scalability, meeting the evolving needs of LLM consumers.
- Further Applications with Context Vectorsby Muhammad Asad Iqbal Khan (MachineLearningMastery.com) on April 18, 2025 at 6:17 pm
This post is divided into three parts; they are: • Building a Semantic Search Engine • Document Clustering • Document Classification If you want to find a specific document within a collection, you might use a simple keyword search.
- Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation modelby Salman Ahmed (AWS Machine Learning Blog) on April 18, 2025 at 5:38 pm
In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.
- Building a RAG Pipeline with llama.cpp in Pythonby Iván Palomares Carrascosa (MachineLearningMastery.com) on April 18, 2025 at 5:35 pm
Using llama.
- Stream ingest data from Kafka to Amazon Bedrock Knowledge Bases using custom connectorsby Prabhakar Chandrasekaran (AWS Machine Learning Blog) on April 18, 2025 at 5:21 pm
For this post, we implement a RAG architecture with Amazon Bedrock Knowledge Bases using a custom connector and topics built with Amazon Managed Streaming for Apache Kafka (Amazon MSK) for a user who may be interested to understand stock price trends.
- Add Zoom as a data accessor to your Amazon Q indexby David Girling (AWS Machine Learning Blog) on April 17, 2025 at 6:19 pm
This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly within their Zoom interface, alleviating the need to switch between applications while maintaining enterprise security boundaries. Organizations can now configure Zoom as a data accessor in Amazon Q Business, enabling seamless integration between their Amazon Q index and Zoom AI Companion. This integration allows users to access their enterprise knowledge in a controlled manner directly within the Zoom platform.
- Detecting & Handling Data Drift in Productionby Jayita Gulati (MachineLearningMastery.com) on April 17, 2025 at 1:59 pm
Machine learning models are trained on historical data and deployed in real-world environments.
- Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Thinkby Nahla Davies (MachineLearningMastery.com) on April 17, 2025 at 12:00 pm
Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.
- Applications with Context Vectorsby Muhammad Asad Iqbal Khan (MachineLearningMastery.com) on April 16, 2025 at 5:22 pm
This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a document based on their contextual relevance.
- Generating and Visualizing Context Vectors in Transformersby Muhammad Asad Iqbal Khan (MachineLearningMastery.com) on April 14, 2025 at 6:04 pm
This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings (such as Word2Vec or GloVe), which assign a fixed vector to each word regardless of context, transformer models generate dynamic representations that depend on surrounding words.
- 5 Lessons Learned Building RAG Systemsby Iván Palomares Carrascosa (MachineLearningMastery.com) on April 14, 2025 at 12:00 pm
Retrieval augmented generation (RAG) is one of 2025's hot topics in the AI landscape.
- Understanding RAG Part X: RAG Pipelines in Productionby Iván Palomares Carrascosa (MachineLearningMastery.com) on April 11, 2025 at 1:59 pm
Be sure to check out the previous articles in this series: •
- Understanding RAG Part IX: Fine-Tuning LLMs for RAGby Iván Palomares Carrascosa (MachineLearningMastery.com) on April 10, 2025 at 1:00 pm
Be sure to check out the previous articles in this series: •
- How to Perform Scikit-learn Hyperparameter Optimization with Optunaby Iván Palomares Carrascosa (MachineLearningMastery.com) on April 9, 2025 at 1:00 pm
Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance.
Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon
A Twitter List by enoumenDownload AWS machine Learning Specialty Exam Prep App on iOs
Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon