Machine Learning

AWS ML Engineer (MLA-C01) Exam Learning Path

AWS Certified Machine Learning Engineer - Associate (MLA-C01) Certificate
~ Last updated on : ~ jayendrapatil

AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Learning Path

🔄 July 2026 Update: Refreshed with Amazon Bedrock AgentCore (GA Oct 2025), SageMaker Lakehouse, SageMaker AI with MLflow 3.0 (serverless), SageMaker HyperPod elastic training, and Amazon Nova models. The AWS Certified Machine Learning – Specialty (MLS-C01) was retired on March 31, 2026, making MLA-C01 the primary AWS certification for ML practitioners.

  • Certified for the last pending AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification, which was newly introduced on October 8, 2024, following its beta period.
  • Machine Learning Engineer – Associate exam validates knowledge to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.
  • Exam also validates a candidate’s ability to complete the following tasks:
    • Ingest, transform, validate, and prepare data for ML modeling.
    • Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions.
    • Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling based on requirements.
    • Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workflows.
    • Monitor models, data, and infrastructure to detect issues.
    • Secure ML systems and resources through access controls, compliance features, and best practices.

Refer AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Guide

AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Summary

  • MLA-C01 exam consists of 65 questions (50 scored and 15 unscored) in 130 minutes, and the time is more than sufficient if you are well-prepared.
  • MLA-C01 exam covers four domains:
    • Domain 1: Data Preparation for Machine Learning (28%)
    • Domain 2: ML Model Development (26%)
    • Domain 3: Deployment and Orchestration of ML Workflows (22%)
    • Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)
  • In addition to the usual types of multiple-choice and multiple-response questions, the MLA-C01 exam has introduced the following new types
    • Ordering: Has a list of 3-5 responses which you need to select and place in the correct order to complete a specified task.
    • Matching: Has a list of responses to match with a list of 3-7 prompts. You must match all the pairs correctly to receive credit for the question.
    • Case study: A case study presents a single scenario with multiple questions. Each question is evaluated independently, and credit is given for each correct answer.
  • MLA-C01 has a scaled score between 100 and 1,000. The scaled score needed to pass the exam is 720.
  • Associate exams currently cost $150 + tax.
  • You can get an additional 30 minutes if English is your second language by requesting Exam Accommodations. It might not be needed for Associate exams but is helpful for Professional and Specialty ones.
  • AWS exams can be taken either remotely or online, I prefer to take them online as it provides a lot of flexibility. Just make sure you have a proper place to take the exam with no disturbance and nothing around you.
  • Also, if you are taking the AWS Online exam for the first time try to join at least 30 minutes before the actual time as I have had issues with both PSI and Pearson with long wait times.

AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Resources

AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Topics

  • AWS Certified Machine Learning Engineer – Associate exam covers a lot of Machine Learning concepts in addition to the AWS ML Services.
  • AWS Certified Machine Learning exam covers the Machine Learning lifecycle, data collection, transformation, making it usable and efficient for Machine Learning, pre-processing data for Machine Learning, training and validation, and implementation.
  • With the Q1 2026 refresh, the exam now includes increased coverage of Amazon Bedrock (Knowledge Bases, Agents, Guardrails, AgentCore), Generative AI workflows, and the unified SageMaker AI platform including Lakehouse and MLflow.

Machine Learning Concepts

  • Exploratory Data Analysis
    • Feature selection and Engineering
      • remove features that are not related to training
      • remove features that have the same values, very low correlation, very little variance, or a lot of missing values
      • Apply techniques like Principal Component Analysis (PCA) for dimensionality reduction i.e. reduce the number of features.
      • Apply techniques such as One-hot encoding and label encoding to help convert strings to numeric values, which are easier to process.
      • Apply Normalization i.e. values between 0 and 1 to handle data with large variance.
      • Apply feature engineering for feature reduction e.g. using a single height/weight feature instead of both features.
    • Handle Missing data
      • remove the feature or rows with missing data
      • impute using Mean/Median values – valid only for Numeric values and not categorical features also does not factor correlation between features
      • impute using k-NN, Multivariate Imputation by Chained Equation (MICE), Deep Learning – more accurate and helps factors correlation between features
    • Handle unbalanced data
      • Source more data
      • Oversample minority or Undersample majority
      • Data augmentation using techniques like Synthetic Minority Oversampling Technique (SMOTE).
  • Modeling
    • Know about Algorithms – Supervised, Unsupervised and Reinforcement and which algorithm is best suitable based on the available data either labelled or unlabelled.
      • Supervised learning trains on labeled data e.g. Linear regression. Logistic regression, Decision trees, Random Forests
      • Unsupervised learning trains on unlabelled data e.g. PCA, SVD, K-means
      • Reinforcement learning trained based on actions and rewards e.g. Q-Learning
    • Hyperparameters
      • are parameters exposed by machine learning algorithms that control how the underlying algorithm operates and their values affect the quality of the trained models
      • some of the common hyperparameters are learning rate, batch, epoch (hint: If the learning rate is too large, the minimum slope might be missed and the graph would oscillate. If the learning rate is too small, it requires too many steps which would take the process longer and is less efficient)
  • Evaluation
    • Know difference in evaluating model accuracy
      • Use Area Under the (Receiver Operating Characteristic) Curve (AUC) for Binary classification
      • Use root mean square error (RMSE) metric for regression
    • Understand Confusion matrix
      • A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class.
      • A false positive is an outcome where the model incorrectly predicts the positive class. A false negative is an outcome where the model incorrectly predicts the negative class.
      • Recall or Sensitivity or TPR (True Positive Rate): Number of items correctly identified as positive out of total true positives- TP/(TP+FN) (hint: use this for cases like fraud detection, cost of marking non fraud as frauds is lower than marking fraud as non-frauds)
      • Specificity or TNR (True Negative Rate): Number of items correctly identified as negative out of total negatives- TN/(TN+FP) (hint: use this for cases like videos for kids, the cost of dropping few valid videos is lower than showing few bad ones)
    • Handle Overfitting problems
      • Simplify the model, by reducing the number of layers
      • Early Stopping – form of regularization while training a model with an iterative method, such as gradient descent
      • Data Augmentation
      • Regularization – technique to reduce the complexity of the model
      • Dropout is a regularization technique that prevents overfitting
      • Never train on test data

Machine Learning Services

SageMaker AI (formerly SageMaker)

Note: At re:Invent 2024, AWS rebranded Amazon SageMaker to Amazon SageMaker AI as part of the next-generation SageMaker platform that unifies data, analytics, and AI. The next-gen SageMaker umbrella includes SageMaker AI, SageMaker Lakehouse, and SageMaker Unified Studio.

  • supports both File mode, Pipe mode, and Fast File mode
    • File mode loads all of the data from S3 to the training instance volumes VS Pipe mode streams data directly from S3
    • File mode needs disk space to store both the final model artifacts and the full training dataset. VS Pipe mode which helps reduce the required size for EBS volumes.
    • Fast File mode combines the ease of use of the existing File Mode with the performance of Pipe Mode.
  • Using RecordIO format allows algorithms to take advantage of Pipe mode when training the algorithms that support it.
  • supports Model tracking capability to manage up to thousands of machine learning model experiments
  • supports automatic scaling for production variants. Automatic scaling dynamically adjusts the number of instances provisioned for a production variant in response to changes in your workload
  • provides pre-built Docker images for its built-in algorithms and the supported deep learning frameworks used for training & inference
  • SageMaker Automatic Model Tuning
    • is the process of finding a set of hyperparameters for an algorithm that can yield an optimal model.
    • Best practices
      • limit the search to a smaller number as the difficulty of a hyperparameter tuning job depends primarily on the number of hyperparameters that Amazon SageMaker has to search
      • DO NOT specify a very large range to cover every possible value for a hyperparameter as it affects the success of hyperparameter optimization.
      • log-scaled hyperparameter can be converted to improve hyperparameter optimization.
      • running one training job at a time achieves the best results with the least amount of compute time.
      • Design distributed training jobs so that they report the objective metric that you want.
  • know how to take advantage of multiple GPUs (hint: increase learning rate and batch size w.r.t to the increase in GPUs)
  • Elastic Inference (deprecated April 2023, replaced by AWS Inferentia) — previously helped attach low-cost GPU-powered acceleration to EC2 and SageMaker instances for deep learning inference. Use AWS Inferentia (Inf2 instances) or AWS Trainium (Trn1/Trn2 instances) for cost-effective ML acceleration.
  • SageMaker AI Inference options.
    • Real-time inference is ideal for online inferences that have low latency or high throughput requirements.
    • Serverless Inference is ideal for intermittent or unpredictable traffic patterns as it manages all of the underlying infrastructure with no need to manage instances or scaling policies.
    • Batch Transform is suitable for offline processing when large amounts of data are available upfront and you don’t need a persistent endpoint.
    • Asynchronous Inference is ideal when you want to queue requests and have large payloads with long processing times.
  • SageMaker AI Model deployment allows deploying multiple variants of a model to the same SageMaker endpoint to test new models without impacting the user experience
    • Production Variants
      • supports A/B or Canary testing where you can allocate a portion of the inference requests to each variant.
      • helps compare production variants’ performance relative to each other.
    • Shadow Variants
      • replicates a portion of the inference requests that go to the production variant to the shadow variant.
      • logs the responses of the shadow variant for comparison and not returned to the caller.
      • helps test the performance of the shadow variant without exposing the caller to the response produced by the shadow variant.
  • SageMaker Managed Spot training can help use spot instances to save cost and with Checkpointing feature can save the state of ML models during training
  • SageMaker Feature Store
    • helps to create, share, and manage features for ML development.
    • is a centralized store for features and associated metadata so features can be easily discovered and reused.
    • now supports Apache Iceberg table format, streaming ingestion, scalable batch ingestion, and fine-grained access control through AWS Lake Formation.
  • SageMaker Debugger provides tools to debug training jobs and resolve problems such as overfitting, saturated activation functions, and vanishing gradients to improve the model’s performance.
  • SageMaker Model Monitor monitors the quality of SageMaker machine learning models in production and can help set alerts that notify when there are deviations in the model quality.
  • SageMaker Automatic Model Tuning helps find a set of hyperparameters for an algorithm that can yield an optimal model.
  • SageMaker Data Wrangler
    • reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes.
    • Note: Data Wrangler has been integrated into Amazon SageMaker Canvas. The new Data Wrangler experience in SageMaker Canvas includes a natural language interface in addition to the visual interface for data exploration and transformation.
  • SageMaker Experiments is a capability of SageMaker that lets you create, manage, analyze, and compare machine learning experiments.
  • SageMaker Clarify helps improve the ML models by detecting potential bias and helping to explain the predictions that the models make.
    • Pre-training bias metrics: CI, DPL, KL Divergence, JS Divergence
    • Post-training bias metrics: DI, DPD, DCA, AD, CDDPL
    • SHAP values for feature importance and individual prediction explanations
    • Foundation model evaluation for accuracy, toxicity, and robustness
    • Integrates with Model Monitor for continuous bias drift detection
  • SageMaker Model Governance is a framework that gives systematic visibility into ML model development, validation, and usage.
  • SageMaker Model Cards
    • helps document critical details about the ML models in a single place for streamlined governance and reporting.
    • helps capture key information about the models throughout their lifecycle and implement responsible AI practices.
  • SageMaker Autopilot
    • is an automated machine learning (AutoML) feature set that automates the end-to-end process of building, training, tuning, and deploying machine learning models.
    • Note: Autopilot UI has been migrated to Amazon SageMaker Canvas. Use SageMaker Canvas for no-code/low-code AutoML capabilities.
  • SageMaker Neo enables machine learning models to train once and run anywhere in the cloud and at the edge.
  • SageMaker API and SageMaker Runtime support VPC interface endpoints powered by AWS PrivateLink that helps connect VPC directly to the SageMaker API or SageMaker Runtime using AWS PrivateLink without using an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.
  • SageMaker managed warm pools retain and reuse provisioned infrastructure after the training job completion to reduce latency for repetitive workloads.
  • SageMaker supports Elastic File System (EFS) and FSx for Lustre file systems as data sources for training machine learning models.
  • SageMaker MLOps
    • ML Lineage Tracking creates and stores tracking information about the steps of a ML workflow from data preparation to model deployment that can help reproduce the workflow steps, track model and dataset lineage, and establish model governance and audit standards.
    • Model Registry provides a model catalog, helps manage model versions, associate metadata, manage model approval status, deploy models to production and share models with other users.

SageMaker AI with MLflow (New – 2024/2025)

  • provides fully managed, serverless MLflow tracking for experiment tracking, model packaging, and model registry — no infrastructure to manage.
  • supports MLflow 3.0 (GA July 2025) with tracing capabilities to record inputs, outputs, and metadata at every step of a generative AI application.
  • helps track multiple training runs as experiments, compare runs with visualizations, evaluate models, and register the best models.
  • models registered in MLflow are automatically registered to SageMaker Model Registry with an associated SageMaker Model Card.
  • supports cross-account sharing, automated version upgrades, and integration with SageMaker Pipelines.
  • integrates with SageMaker HyperPod to track foundation model training experiments at scale.

SageMaker HyperPod (New – 2023/2024/2025)

  • purpose-built infrastructure for training and inference of foundation models at scale, reducing training time by up to 40%.
  • efficiently distributes and parallelizes training workloads across hundreds or thousands of AI accelerators (GPUs/Trainium chips).
  • continuously checks for hardware problems, resolves them automatically, and ensures workloads recover without manual intervention.
  • key features:
    • Checkpointless Training — maintains forward training progress despite failures using peer-to-peer transfer of model and optimizer states from healthy accelerators, enabling 95%+ training goodput and 80-93% reduction in recovery time.
    • Elastic Training (Dec 2025) — automatically adjusts the number of data-parallel replicas based on cluster utilization and priority of workloads.
    • Flexible Training Plans — helps meet training timelines and budgets with automated capacity planning.
    • Amazon EKS Integration — orchestrates HyperPod clusters via Amazon EKS, supporting containerized workloads, PyTorchJob auto-resume, and inference on the same cluster.
    • Interactive IDEs — supports running interactive workloads alongside training jobs with fractional GPU allocations.
  • integrates with Amazon CloudWatch Container Insights, Amazon Managed Prometheus, and Amazon Managed Grafana for observability.

SageMaker Lakehouse (New – re:Invent 2024)

  • a unified, open, and secure data lakehouse architecture built on Apache Iceberg that unifies data across S3 data lakes and Amazon Redshift data warehouses.
  • enables running analytics and ML workloads — including Apache Spark jobs, SQL dashboards, ML models, and generative AI applications — on a single copy of data.
  • key capabilities:
    • Unified access to Amazon S3 data lakes (including S3 Tables), Amazon Redshift data warehouses, and operational databases via zero-ETL integrations.
    • Fine-grained access control through AWS Lake Formation with consistent permissions across all query engines.
    • Compatible with Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift for querying and analysis.
    • Fully compatible with Apache Iceberg REST API for open interoperability.
  • relevant to MLA-C01 Domain 1 (Data Preparation) — provides unified data access for ML feature engineering and training data preparation.

SageMaker Unified Studio (New – re:Invent 2024)

  • a unified interface combining data preparation, ML model development, generative AI, and governance.
  • integrates SageMaker AI, Amazon Bedrock, analytics, and data governance into a single platform.
  • supports one-click onboarding with existing IAM roles and permissions.
  • provides collaborative workspace for data engineers, ML engineers, and business analysts.

SageMaker Canvas

  • a visual, no-code/low-code ML service that enables building, evaluating, and deploying production-ready models without writing code.
  • now integrates Data Wrangler and Autopilot capabilities.
  • supports petabyte-scale data preparation and time series forecasting (replacing Amazon Forecast).
  • supports fine-tuning foundation models via Amazon Bedrock integration.

SageMaker Ground Truth

  • provides automated data labeling using machine learning
  • helps build highly accurate training datasets for machine learning quickly using Amazon Mechanical Turk
  • provides annotation consolidation to help improve the accuracy of the data object’s labels. It combines the results of multiple worker’s annotation tasks into one high-fidelity label.
  • automated data labeling uses machine learning to label portions of the data automatically without having to send them to human workers

Amazon Bedrock (Critical for 2026 Exam)

  • Amazon Bedrock is a fully managed service providing access to foundation models (FMs) from Amazon and third-party providers through a unified API.
    • supports models from Amazon (Nova, Titan), Anthropic (Claude), Meta (Llama), Mistral, Cohere, and others.
    • provides serverless experience — no infrastructure to manage.
  • Bedrock Knowledge Bases
    • enables RAG (Retrieval-Augmented Generation) by grounding FM responses in enterprise data.
    • automatically chunks documents, creates embeddings, and stores them in a vector database.
    • supports vector stores including Amazon OpenSearch Serverless, Amazon Aurora, Pinecone, and Redis Enterprise.
    • Managed Knowledge Base (GA June 2026) — fully managed RAG without managing vector databases or data pipelines.
    • integrates with Amazon Kendra GenAI Index for enhanced semantic retrieval.
  • Bedrock Agents
    • orchestrate multi-step generative AI workflows by connecting FMs to APIs and data sources.
    • automatically break down tasks, create orchestration plans, and execute actions.
    • support action groups (Lambda functions) and knowledge base integration.
  • Bedrock AgentCore (Preview Jul 2025, GA Oct 2025)
    • a dedicated platform to build, deploy, and operate AI agents at any scale with enterprise-grade security.
    • framework-agnostic and model-agnostic — works with any open-source agent framework (LangChain, CrewAI, AutoGen) and any model.
    • key components:
      • AgentCore Runtime — serverless, scalable environment to host and run agents without managing infrastructure; supports VPC, PrivateLink, and CloudFormation.
      • AgentCore Tools — pre-built tools including Code Interpreter, browser automation, and MCP (Model Context Protocol) connectors for enterprise data.
      • AgentCore Identity — manages authentication and fine-grained access control for agents connecting to third-party tools and data sources.
      • AgentCore Observability — tracing and monitoring for agent execution in production.
      • AgentCore Harness (GA 2026) — go from idea to production-grade agent in minutes with automated container packaging and deployment.
    • now included in MLA-C01 exam scope per Q1 2026 refresh.
  • Bedrock Guardrails
    • configurable safeguards to filter harmful content, block sensitive data (PII), and ensure compliance.
    • enforces deterministic controls independent of model’s reasoning quality.
    • supports content filters, denied topics, word filters, sensitive information filters, and contextual grounding checks.
    • can be associated with agents, knowledge bases, and direct model invocations.
  • Bedrock Model Evaluation
    • evaluate, compare, and select the best FM for a specific use case.
    • supports automatic evaluation (built-in metrics) and human evaluation.
  • Bedrock Model Customization
    • supports continued pre-training and fine-tuning of FMs with proprietary data.
    • supports Reinforcement Fine-Tuning (RFT) for Amazon Nova and open-source models.
    • Custom Model Import allows bringing SageMaker-trained models into Bedrock for serverless inference.
    • Model Distillation enables creating smaller, faster, cost-effective models from larger teacher models (e.g., Nova Premier → Nova Pro/Lite/Micro).
  • Bedrock Flows
    • visual builder for creating generative AI workflows connecting prompts, models, knowledge bases, and agents.

Amazon Nova Models

  • Amazon’s own family of foundation models available exclusively in Amazon Bedrock, launched at re:Invent 2024.
    • Nova Micro — text-only, fastest and lowest cost; optimized for summarization, translation, classification, and simple reasoning. 128K context.
    • Nova Lite — multimodal (text, image, video input → text output); cost-efficient for simple automation and document processing.
    • Nova Pro — multimodal; balanced capability, accuracy, speed, and cost for a wide range of tasks including agentic workflows.
    • Nova Premier — most capable; excels at complex tasks requiring deep context understanding, multistep planning, and multi-tool execution. Ideal as a teacher model for distillation.
  • All Nova models support fine-tuning and model distillation through Amazon Bedrock.
  • Nova 2 family (2025) offers next-generation improvements in intelligence and cost-efficiency.

Machine Learning & AI Managed Services

  • Comprehend
    • natural language processing (NLP) service to find insights and relationships in text.
    • identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic.
  • Rekognition – analyze images and video to identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content.
  • Transcribe – automatic speech recognition (ASR) speech-to-text
  • Kendra – an intelligent search service that uses NLP and advanced ML algorithms to return specific answers to search questions from your data.
    • Kendra GenAI Index (New – re:Invent 2024) — delivers highest accuracy for RAG and intelligent search using latest information retrieval technologies and semantic models.
    • integrates with Amazon Q Business and Amazon Bedrock Knowledge Bases.
  • Augmented AI (Amazon A2I) is an ML service that makes it easy to build the workflows required for human review.
  • Amazon Q Developer (formerly CodeWhisperer) — AI-powered coding assistant for building, deploying, and operating applications on AWS.

Generative AI

Refer: AWS AI & Generative AI Services Cheat Sheet

  • MLA-C01 covers Generative AI concepts with increased emphasis post-Q1 2026 refresh, including practical AWS Bedrock integration.
  • Foundation Models:
    • Large, pre-trained models built on diverse data that can be fine-tuned for specific tasks like text, image, and speech generation. for e.g. GPT, BERT, DALL·E, Amazon Nova, Claude, Llama.
  • Large Language Models (LLMs):
    • A subset of foundation models designed to understand and generate human-like text. Capable of answering questions, summarizing, translating, and more.
    • LLM Components
      • Tokens: Basic units of text (words, subwords, or characters) that LLMs process.
      • Vectors: Numerical representations of tokens in high-dimensional space, enabling the model to perform mathematical operations on text.
      • Embeddings: Pre-trained numerical vector representations of tokens that capture their semantic meaning.
      • Attention Mechanism: Allows models to weigh the importance of different tokens in a sequence relative to each other (e.g., self-attention in Transformers).
  • Prompt Engineering:
    • Crafting effective input instructions to guide generative AI toward desired outputs. Key for improving performance without fine-tuning the model.
    • Techniques include zero-shot, few-shot, chain-of-thought (CoT), and ReAct prompting.
  • Retrieval-Augmented Generation (RAG):
    • Combines LLMs with external knowledge bases to retrieve accurate and up-to-date information during text generation.
    • Reduces hallucinations by grounding responses in verified enterprise data.
    • AWS Implementation: Amazon Bedrock Knowledge Bases + vector stores.
  • Fine-Tuning:
    • Adjusting pre-trained models using domain-specific data to optimize performance for specific applications.
    • AWS options: Amazon Bedrock fine-tuning, SageMaker AI fine-tuning (SFT, DPO, RLVR, RLAIF).
  • Responsible AI Features:
    • Incorporates fairness, transparency, and bias mitigation techniques to ensure ethical AI outputs.
    • Amazon Bedrock Guardrails provides managed responsible AI controls.
  • Multi-Modal Capabilities:
    • Models that process and generate outputs across multiple data types, such as text, images, and audio.
    • Amazon Nova models support text, image, and video generation.
  • Controls
    • Temperature: Adjusts randomness in the output; lower values (e.g., 0.2) produce focused and deterministic results, while higher values (e.g., 1.0+) generate creative and diverse outputs.
    • Top P (Nucleus Sampling): Determines the probability threshold for token selection — with Top P = 0.9, the model considers only the smallest set of tokens whose cumulative probability is 90%.
    • Top K: Limits the token selection to the top K most probable tokens — with Top K = 10, the model randomly chooses tokens only from the 10 most likely options.
    • Token Length (Max Tokens): Sets the maximum number of tokens the model can generate in a response.

Analytics

  • Kinesis
  • Glue is a fully managed, ETL (extract, transform, and load) service that automates the time-consuming steps of data preparation for analytics
    • helps setup, orchestrate, and monitor complex data flows.
    • Glue Data Catalog is a central repository to store structural and operational metadata for all the data assets.
    • Glue crawler connects to a data store, extracts the schema of the data, and then populates the Glue Data Catalog with this metadata
    • Glue DataBrew is a visual data preparation tool that enables users to clean and normalize data without writing any code.

Security, Identity & Compliance

  • SageMaker can read data from KMS-encrypted S3. Make sure, the KMS key policies include the role attached with SageMaker
  • Amazon Bedrock supports AWS PrivateLink for private connectivity, encryption at rest and in transit, and IAM-based access control.
  • Bedrock AgentCore supports VPC isolation, PrivateLink, and fine-grained IAM permissions for agent-to-tool authentication.

Management & Governance Tools

  • Understand AWS CloudWatch for Logs and Metrics. (hint: SageMaker is integrated with CloudWatch and logs and metrics are all stored in it)

Deprecated Services to Be Aware Of

  • Amazon Elastic Inference — deprecated April 2023. Use AWS Inferentia (Inf2 instances) or AWS Trainium (Trn1/Trn2 instances) for cost-effective ML inference/training acceleration.
  • SageMaker Edge Manager — discontinued April 26, 2024. Use AWS IoT Greengrass V2 with ONNX format for edge ML deployments.
  • Amazon Forecast — closed to new customers July 29, 2024. Use SageMaker Canvas for time series forecasting.
  • SageMaker Studio Classic — replaced by the updated SageMaker Studio experience and SageMaker Unified Studio.
  • AWS Certified Machine Learning – Specialty (MLS-C01) — retired March 31, 2026. MLA-C01 is now the primary ML certification.

Practice Questions (2025-2026 Updates)

  1. A company is training a large language model with 70 billion parameters across 256 GPUs. During training, individual GPU nodes occasionally fail, causing the entire job to restart from the last checkpoint. This adds days to the training timeline. Which AWS service minimizes the impact of hardware failures on foundation model training?
    1. Amazon SageMaker AI training jobs with managed spot instances
    2. Amazon SageMaker HyperPod with checkpointless training
    3. Amazon EC2 P5 instances with EBS snapshots
    4. AWS Batch with retry strategies
    Show Answer

    Answer: B. – SageMaker HyperPod with checkpointless training automatically detects and replaces faulty nodes, then recovers training using peer-to-peer transfer of model states from healthy accelerators — achieving 95%+ training goodput and 80-93% reduction in recovery time without relying on checkpoint files.

  2. A machine learning team wants to deploy an AI agent that can access company databases, execute code, and call external APIs autonomously. The agent must run in a production environment with enterprise security controls, VPC isolation, and the ability to scale to thousands of concurrent requests. The team uses LangChain as their agent framework. Which AWS service should they use?
    1. Amazon Bedrock Agents
    2. Amazon Bedrock AgentCore
    3. AWS Lambda with Amazon Bedrock API calls
    4. Amazon ECS with a custom agent container
    Show Answer

    Answer: B. – Amazon Bedrock AgentCore provides a framework-agnostic (including LangChain) and model-agnostic platform for deploying production-grade AI agents with serverless scaling, VPC/PrivateLink support, built-in identity management for tool authentication, and observability — all without managing infrastructure.

  3. A data science team needs to track experiments across multiple foundation model fine-tuning runs on SageMaker HyperPod, compare metrics visually, and automatically register the best model for deployment. They want to avoid managing any tracking infrastructure. Which solution meets these requirements with LEAST operational overhead?
    1. Deploy a self-managed MLflow server on Amazon EC2 and configure SageMaker to log metrics
    2. Use Amazon SageMaker AI with serverless MLflow and SageMaker Model Registry integration
    3. Use Amazon CloudWatch custom metrics with CloudWatch dashboards
    4. Use SageMaker Experiments with manual model registration
    Show Answer

    Answer: B. – SageMaker AI with serverless MLflow provides fully managed, infrastructure-free experiment tracking that integrates with HyperPod, automatically scales, and automatically registers models to SageMaker Model Registry with an associated Model Card — the least operational overhead for this workflow.

  4. A company wants to build a RAG-based chatbot that answers questions using internal documents. They need to ensure the chatbot never reveals personally identifiable information (PII) from the documents and does not generate responses about competitors. Which combination of Amazon Bedrock features should they use? (Choose TWO)
    1. Bedrock Knowledge Bases
    2. Bedrock Model Customization with continued pre-training
    3. Bedrock Guardrails with sensitive information filters and denied topics
    4. Bedrock AgentCore Code Interpreter
    5. Amazon Comprehend PII detection
    Show Answer

    Answer: A, C – Bedrock Knowledge Bases provides the RAG capability to ground responses in internal documents. Bedrock Guardrails with sensitive information filters (PII blocking) and denied topics (competitor mentions) enforces deterministic controls to prevent PII leakage and off-topic responses regardless of the model’s reasoning.

  5. An organization is preparing ML training datasets that span data in Amazon S3 data lakes, Amazon Redshift data warehouses, and Amazon DynamoDB operational tables. They want to access all this data through a unified interface with consistent fine-grained access controls for their SageMaker AI training jobs. Which service provides this unified data access?
    1. AWS Glue Data Catalog with Lake Formation permissions
    2. Amazon SageMaker Lakehouse
    3. Amazon Athena federated queries
    4. Amazon Redshift Spectrum
    Show Answer

    Answer: B. – Amazon SageMaker Lakehouse provides a unified, open (Apache Iceberg-based) data lakehouse that unifies access to S3 data lakes (including S3 Tables), Redshift data warehouses, and operational databases via zero-ETL integrations — all with consistent fine-grained access controls through AWS Lake Formation.

Whitepapers and Articles

Related AWS AI/ML Certifications

  • AWS Certified AI Practitioner (AIF-C01) — foundational-level certification for understanding AI/ML concepts and AWS AI services.
  • AWS Certified Generative AI Developer – Professional (AIP-C01) — professional-level certification for building production-ready generative AI solutions using Amazon Bedrock. Launched late 2025 (GA April 2026).
  • AWS Certified Machine Learning – Specialty (MLS-C01) — retired March 31, 2026. Holders retain active certification for 3 years from date earned.

AWS Architecture Patterns for MLA-C01

On the Exam Day

  • Make sure you are relaxed and get some good night’s sleep. The exam is not tough if you are well-prepared.
  • If you are taking the AWS Online exam
    • Try to join at least 30 minutes before the actual time as I have had issues with both PSI and Pearson with long wait times.
    • The online verification process does take some time and usually, there are glitches.
    • Remember, you would not be allowed to take the take if you are late by more than 30 minutes.
    • Make sure you have your desk clear, no hand-watches, or external monitors, keep your phones away, and nobody can enter the room.

Finally, All the Best 🙂

AWS AI & Machine Learning Services Cheat Sheet

AWS Machine Learning Services
~ Last updated on : ~ jayendrapatil ~ 1 Comment

AWS Machine Learning Services

AWS Machine Learning Services

Amazon Bedrock

  • is a fully managed service providing access to high-performing foundation models (FMs) from leading AI companies (GA September 2023).
  • offers foundation models from AI21 Labs, Amazon (Nova), Anthropic, Cohere, Meta, Mistral AI, OpenAI, and Stability AI through a unified API.
  • enables building and scaling generative AI applications without managing infrastructure.
  • supports model customization including fine-tuning and reinforcement fine-tuning (RFT) with your own data while maintaining data privacy and security.
  • provides serverless experience with pay-per-use pricing.
  • includes capabilities for text generation, chat, image generation, video generation, and embeddings.
  • supports Retrieval Augmented Generation (RAG) with Knowledge Bases and the new Managed Knowledge Base (2026) that abstracts storage, retrieval, embeddings, and re-ranking into a single managed primitive.
  • provides Bedrock Agents for multi-step task automation.
  • includes Amazon Bedrock Guardrails for configurable safety controls including content filtering, topic classification, sensitive information protection, and hallucination detection across both text and images with up to 88% harmful content blocking accuracy.
  • supports OpenAI-compatible API endpoints (2026) including Responses API and Chat Completions API for simplified migration and integration.
  • ensures data is not used to train base models and remains within your AWS environment.
  • includes Amazon Bedrock AgentCore (2026) — a platform to build, connect, deploy, and optimize AI agents with managed harness, observability, guardrails integration, and continuous optimization capabilities.

Amazon Nova Foundation Models

  • is Amazon’s family of proprietary foundation models available exclusively through Amazon Bedrock (launched December 2024 at re:Invent).
  • includes Amazon Nova Micro — a text-only model optimized for speed and lowest cost, ideal for summarization, translation, and classification (128K context).
  • includes Amazon Nova Lite — a low-cost multimodal model processing text, images, and video for tasks like document analysis and visual Q&A.
  • includes Amazon Nova Pro — a balanced multimodal model offering strong accuracy, speed, and cost for a wide range of tasks.
  • includes Amazon Nova Premier — the most capable model for complex reasoning, agentic workflows, and model distillation.
  • includes Amazon Nova Canvas — an image generation model.
  • includes Amazon Nova Reel — a video generation model.
  • includes Amazon Nova Sonic — a speech-to-speech model.
  • Amazon Nova 2 models (Nova 2 Lite and Nova 2 Pro) announced in December 2025 with improved capabilities.
  • all Nova models are among the fastest and most cost-effective in their respective intelligence classes, optimized for RAG and agentic applications.

Amazon Q Developer (formerly CodeWhisperer) → Transitioning to Kiro

  • is a generative AI-powered coding assistant for software developers (rebranded from CodeWhisperer in April 2024).
  • provides real-time code suggestions, completions, and generation based on comments and existing code.
  • supports multiple programming languages including Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell, SQL, and more.
  • integrates with popular IDEs including VS Code, IntelliJ IDEA, PyCharm, WebStorm, and AWS Cloud9.
  • performs security scanning to identify and suggest fixes for vulnerabilities.
  • provides code explanations and documentation generation.
  • assists with debugging, upgrading applications, and troubleshooting.
  • tracks open-source code references and license information.
  • offers free tier for individual developers and paid tier for professional use.
⚠️ Transition Notice (May 2026): Amazon Q Developer IDE plugins and paid subscriptions will reach end-of-support on April 30, 2027. New signups blocked as of May 15, 2026. The successor is Kiro — AWS’s next-generation agentic development environment (IDE and CLI) built on Code OSS and powered by Amazon Bedrock. Kiro includes agentic coding, inline chat, terminal integration, and MCP support. Users have a 12-month transition window.

Amazon Quick (formerly Amazon Q Business)

  • is a generative AI-powered assistant for enterprise use, rebranded from Amazon Q Business to Amazon Quick in April 2026.
  • is described as “the next evolution of Amazon Q Business” — an AI assistant for work that connects to apps, learns workflows, and takes action.
  • answers questions, provides summaries, generates content, and completes tasks based on enterprise data.
  • connects to 40+ enterprise data sources including S3, SharePoint, Salesforce, ServiceNow, Jira, and more.
  • respects existing access controls and permissions from connected data sources.
  • provides conversational interface for employees to access company information.
  • available as a desktop app (Windows and Mac) with Microsoft 365 extensions (Outlook, Word, Teams).
  • offers Free and Plus pricing plans.
  • supports autonomous agents for handling recurring tasks continuously.
  • supports Amazon Q Apps for creating AI-powered applications from conversations.
  • ensures enterprise data privacy and security with data isolation.

Amazon SageMaker AI (formerly Amazon SageMaker)

  • Naming Update (December 2024): On December 3, 2024, Amazon SageMaker was renamed to Amazon SageMaker AI. The “SageMaker” brand now refers to the next-generation unified platform for data, analytics, and AI.
  • Build, train, and deploy machine learning models at scale.
  • fully-managed service that enables data scientists and developers to quickly and easily build, train & deploy machine learning models.
  • enables developers and scientists to build machine learning models for use in intelligent, predictive apps.
  • is designed for high availability with no maintenance windows or scheduled downtimes.
  • allows users to select the number and type of instance used for the hosted notebook, training & model hosting.
  • can be deployed as endpoint interfaces and batch.
  • supports Canary deployment using ProductionVariant and deploying multiple variants of a model to the same SageMaker HTTPS endpoint.
  • supports Jupyter notebooks.
  • Users can persist their notebook files on the attached ML storage volume.
  • Users can modify the notebook instance and select a larger profile through the SageMaker console, after saving their files and data on the attached ML storage volume.
  • includes built-in algorithms for linear regression, logistic regression, k-means clustering, principal component analysis, factorization machines, neural topic modeling, latent dirichlet allocation, gradient boosted trees, seq2seq, time series forecasting, word2vec & image classification
  • algorithms work best when using the optimized protobuf recordIO format for the training data, which allows Pipe mode that streams data directly from S3 and helps faster start times and reduce space requirements
  • provides built-in algorithms, pre-built container images, or extend a pre-built container image and even build your custom container image.
  • supports users custom training algorithms provided through a Docker image adhering to the documented specification.
  • also provides optimized MXNet, Tensorflow, Chainer & PyTorch containers
  • ensures that ML model artifacts and other system artifacts are encrypted in transit and at rest.
  • requests to the API and console are made over a secure (SSL) connection.
  • stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.
  • SageMaker Neo is a capability that enables machine learning models to train once and run anywhere in the cloud and at the edge.

Amazon SageMaker Unified Studio

  • is a unified web-based development environment announced at re:Invent 2024 and GA in March 2025.
  • is part of the next generation of Amazon SageMaker — the center for all data, analytics, and AI.
  • breaks down silos in data and tools, giving data engineers, data scientists, data analysts, and ML developers a single development experience.
  • brings together functionality from Amazon EMR, AWS Glue, Amazon Redshift, Amazon Bedrock, and SageMaker AI Studio.
  • enables discovering data and AI assets from across the organization, then collaborating in projects to securely build and share analytics and AI artifacts.
  • includes SageMaker Lakehouse — unifies data across data lakes, data warehouses, operational databases, and enterprise applications with Apache Iceberg compatibility.
  • includes SageMaker Data and AI Governance for integrated access controls and data governance.
  • offers choice of IDEs including JupyterLab, Code Editor (based on VS Code OSS), and RStudio.
  • Note: The previous “SageMaker Studio” experience was renamed to “SageMaker Studio Classic” (November 2023) and is now part of SageMaker AI.

Amazon SageMaker Canvas

  • is a no-code machine learning service for business analysts (launched November 2021).
  • enables building accurate ML models without writing code or requiring ML expertise.
  • provides visual, point-and-click interface for data preparation and model building.
  • supports tabular, image, and text data for predictions.
  • connects to 50+ data sources including S3, Redshift, Snowflake, and SaaS applications.
  • offers ready-to-use ML models and custom model building capabilities.
  • includes generative AI capabilities (October 2023) for text generation, summarization, and content creation.
  • provides automated feature engineering, algorithm selection, and hyperparameter tuning.
  • enables one-click model deployment and batch predictions.
  • supports collaboration between business analysts and data scientists.
  • is the recommended migration path for Amazon Forecast customers for time-series forecasting.

Amazon SageMaker Clarify

  • provides bias detection, model explainability, and foundation model evaluation capabilities.
  • detects pre-training bias (Class Imbalance, DPL, KL Divergence) and post-training bias (Disparate Impact, Demographic Parity Difference).
  • provides SHAP-based feature importance for individual predictions and partial dependence plots.
  • evaluates foundation models for accuracy, robustness, toxicity, and stereotyping.
  • integrates with Model Monitor for continuous bias drift detection in production.
  • identifies biases in training data and ML models across different groups (age, gender, income, etc.).
  • detects potential bias during data preparation, after model training, and in deployed models.
  • generates detailed reports quantifying different types of possible bias.
  • provides feature importance graphs to explain model predictions.
  • integrates with SageMaker Data Wrangler for bias detection during data preparation.
  • supports continuous monitoring of deployed models for bias drift.
  • helps meet regulatory requirements and ethical AI standards.
  • produces reports for internal presentations and compliance documentation.

Amazon SageMaker HyperPod

  • is purpose-built infrastructure for distributed training at scale (GA November 2023).
  • reduces time to train foundation models by up to 40% with optimized infrastructure.
  • supports GPU-based and AWS Trainium-based instances for cost-effective training.
  • provides automated cluster health monitoring and node replacement.
  • enables training for weeks or months with automated resiliency.
  • automatically saves checkpoints and resumes training from last checkpoint on failure.
  • efficiently distributes models and data across thousands of compute resources.
  • includes preconfigured distributed training libraries for popular frameworks.
  • provides recipes for accelerating foundation model training and fine-tuning.
  • offers flexible training plans to meet timelines and budgets.

Amazon Textract

  • Textract provides OCR and helps add document text detection and analysis to the applications.
  • includes simple, easy-to-use API operations that can analyze image files and PDF files.
  • extracts text, handwriting, tables, and forms from scanned documents.
  • supports Queries for extracting specific information from documents using natural language questions.
  • provides Lending API for automated mortgage document processing.

Amazon Comprehend

  • Comprehend is a managed natural language processing (NLP) service to find insights and relationships in text.
  • identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic.
  • can analyze a collection of documents and other text files (such as social media posts) and automatically organize them by relevant terms or topics.
  • supports custom entity recognition and custom classification for domain-specific NLP.
  • provides Comprehend Medical for extracting medical information such as conditions, medications, dosages, and their relationships.
⚠️ Note (April 2026): Amazon Comprehend topic modeling, event detection, and prompt safety classification features are no longer available to new customers as of April 30, 2026. Existing customers can continue to use these features.

Amazon Lex

  • is a service for building conversational interfaces using voice and text.
  • provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable building applications with highly engaging user experiences and lifelike conversational interactions.
  • common use cases of Lex include: Application/Transactional bot, Informational bot, Enterprise Productivity bot, and Device Control bot.
  • leverages Lambda for Intent fulfillment, Cognito for user authentication & Polly for text-to-speech.
  • scales to customers’ needs and does not impose bandwidth constraints.
  • is a completely managed service so users don’t have to manage the scaling of resources or maintenance of code.
  • uses deep learning to improve over time.
  • supports Generative AI features powered by Amazon Bedrock LLMs including:
    • AMAZON.QnAIntent — handles FAQ-style questions using knowledge bases without configuring individual intents.
    • Assisted NLU (2025) — uses LLMs to improve intent classification and slot resolution accuracy while staying within configured intents.
    • Descriptive Bot Builder — generates bot configurations from natural language descriptions.

Amazon Polly

  • text into speech
  • uses advanced deep-learning technologies to synthesize speech that sounds like a human voice.
  • provides dozens of lifelike voices across 60+ languages.
  • supports multiple voice engines:
    • Standard — concatenative synthesis voices.
    • Neural — higher-quality neural TTS voices.
    • Long-Form — optimized for long content like articles and books.
    • Generative (2024-2025) — the most natural-sounding voices using generative AI, with new voices continually added.
  • supports Lexicons to customize pronunciation of specific words & phrases.
  • supports Speech Synthesis Markup Language (SSML) tags like prosody so users can adjust the speech rate, pitch, pauses, or volume.
  • supports bidirectional streaming API for real-time applications.

Amazon Rekognition

  • analyzes image and video
  • identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content.
  • provides highly accurate facial analysis and facial search capabilities that can be used to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
  • helps identify potentially unsafe or inappropriate content across both image and video assets and provides detailed labels that help accurately control what you want to allow based on your needs.
  • provides Rekognition Custom Labels (launched December 2019) – an AutoML feature to build custom ML models for detecting specific objects and scenes unique to business needs.
  • Custom Labels requires as few as 10 sample images per label to train custom models.
  • Custom Labels automatically selects optimal ML algorithms and trains models without requiring ML expertise.
  • enables identifying business-specific items like machine parts, product defects, or brand logos.

Amazon Forecast

⚠️ SERVICE CLOSED TO NEW CUSTOMERS (July 29, 2024)
Amazon Forecast is no longer available to new customers. Existing customers can continue using the service. Migration: Use Amazon SageMaker Canvas for time-series forecasting with a no-code interface.
  • Amazon Forecast is a fully managed time-series forecasting service that uses statistical and machine learning algorithms to deliver highly accurate time-series forecasts and is built for business metrics analysis.
  • automatically tracks the accuracy of the model over time as new data is imported.
  • provides six built-in algorithms which include ARIMA, Prophet, NPTS, ETS, CNN-QR, and DeepAR+.
  • integrates with AutoML to choose the optimal model for the datasets.

Amazon SageMaker Ground Truth

  • helps build highly accurate training datasets for machine learning quickly.
  • offers easy access to labelers through Amazon Mechanical Turk and provides them with built-in workflows and interfaces for common labeling tasks.
  • allows using your own labelers or use vendors recommended by Amazon through AWS Marketplace.
  • helps lower labeling costs by up to 70% using automatic labeling, which works by training Ground Truth from data labeled by humans so that the service learns to label data independently.
  • provides annotation consolidation to help improve the accuracy of the data object’s labels.

Amazon Translate

  • provides natural and fluent language translation
  • a neural machine translation service that delivers fast, high-quality, and affordable language translation.
  • Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and natural-sounding translation than traditional statistical and rule-based translation algorithms.
  • allows content localization – such as websites and applications – for international users, and to easily translate large volumes of text efficiently.

Amazon Transcribe

  • provides speech-to-text capability
  • uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately.
  • can be used to transcribe customer service calls, automate closed captioning and subtitling, and generate metadata for media assets to create a fully searchable archive.
  • adds punctuation and formatting so that the output closely matches the quality of manual transcription at a fraction of the time and expense.
  • process audio in batch or near real-time.
  • supports automatic language identification.
  • supports custom vocabulary to generate more accurate transcriptions for domain-specific words and phrases like product names, technical terminology, or names of individuals.
  • supports specifying a list of words to remove from transcripts.
  • provides Transcribe Call Analytics (launched August 2021) for extracting insights from customer conversations.
  • Call Analytics generates turn-by-turn transcripts with speaker identification and sentiment analysis.
  • supports real-time Call Analytics (November 2022) for live conversation insights and agent assistance.
  • provides Transcribe Medical for healthcare and medical transcription with HIPAA eligibility.

Amazon Kendra

  • is an intelligent search service that uses NLP and advanced ML algorithms to return specific answers to search questions from your data.
  • uses its semantic and contextual understanding capabilities to decide whether a document is relevant to a search query.
  • returns specific answers to questions, giving users an experience that’s close to interacting with a human expert.
  • provides a unified search experience by connecting multiple data repositories to an index and ingesting and crawling documents.
  • can use the document metadata to create a feature-rich and customized search experience for the users, helping them efficiently find the right answers to their queries.
  • can be used as a retriever for Amazon Quick (formerly Amazon Q Business) to power enterprise search with generative AI.

Augmented AI (Amazon A2I)

  • Augmented AI (Amazon A2I) is an ML service that makes it easy to build the workflows required for human review.
  • brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers, whether it runs on AWS or not.
  • integrates with Amazon Textract for document processing and Amazon Rekognition for content moderation.
  • supports private review teams, Amazon Mechanical Turk, and AWS Marketplace vendors.

Amazon Personalize

  • Personalize is a fully managed machine learning service that uses data to generate item recommendations.
  • can also generate user segments based on the users’ affinity for certain items or item metadata.
  • generates recommendations primarily based on item interaction data that comes from the users interacting with items in the catalog.
  • includes API operations for real-time personalization, and batch operations for bulk recommendations and user segments.

Amazon Panorama

⚠️ SERVICE END OF SUPPORT — May 31, 2026
AWS will end support for AWS Panorama on May 31, 2026. After this date, you will no longer be able to access the AWS Panorama console or resources, and Panorama devices will become non-functional. Consider migrating to Amazon SageMaker AI with edge deployment or third-party edge CV solutions.
  • brings computer vision to the on-premises camera network.
  • AWS Panorama Appliance or another compatible device can be installed in the data center and registered with AWS Panorama to deploy computer vision applications from the cloud.
  • AWS Panorama Appliance
    • is a compact edge appliance that uses a powerful system-on-module (SOM) that is optimized for ML workloads.
    • can run multiple computer vision models against multiple video streams in parallel and output the results in real-time.
    • is designed for use in commercial and industrial settings and is rated for dust and liquid protection.
  • works with the existing real-time streaming protocol (RTSP) network cameras.

Amazon Fraud Detector

  • Fraud Detector is a fully managed service to identify potentially fraudulent online activities such as online payment fraud and fake account creation.
  • takes care of all the heavy lifting such as data validation and enrichment, feature engineering, algorithm selection, hyperparameter tuning, and model deployment.

AWS IoT Greengrass ML Inference

  • IoT Greengrass helps perform machine learning inference locally on devices, using models that are created, trained, and optimized in the cloud.
  • provides flexibility to use machine learning models trained in SageMaker or to bring your pre-trained model stored in S3.
  • helps get inference results with very low latency to ensure the IoT applications can respond quickly to local events.

Amazon Elastic Inference

⚠️ SERVICE DEPRECATED (April 2023)
Amazon Elastic Inference is no longer available to new customers. Alternatives: Use AWS Inferentia instances (Inf1/Inf2) for better price-performance on inference workloads, or use SageMaker AI real-time inference endpoints with appropriate instance types.
  • helped attach low-cost GPU-powered acceleration to EC2 and SageMaker instances or ECS tasks to reduce the cost of running deep learning inference by up to 75%.
  • supported TensorFlow, Apache MXNet, and ONNX models.

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. A company has built a deep learning model and now wants to deploy it using the SageMaker Hosting Services. For inference, they want a cost-effective option that guarantees low latency but still comes at a fraction of the cost of using a GPU instance for your endpoint. As a machine learning Specialist, what feature should be used?
    1. Inference Pipeline
    2. Elastic Inference [Note: Elastic Inference is deprecated. Current recommendation is AWS Inferentia (Inf2) instances for cost-effective inference.]
    3. SageMaker Ground Truth
    4. SageMaker Neo
  2. A machine learning specialist works for an online retail company that sells health products. The company allows users to enter reviews of the products they buy from the website. The company wants to make sure the reviews do not contain any offensive or unsafe content, such as obscenities or threatening language. Which Amazon SageMaker algorithm or service will allow scanning user’s review text in the simplest way?
    1. BlazingText
    2. Transcribe
    3. Semantic Segmentation
    4. Comprehend
  3. A company develops a tool whose coverage includes blogs, news sites, forums, videos, reviews, images, and social networks such as Twitter and Facebook. Users can search data by using Text and Image Search, and use charting, categorization, sentiment analysis, and other features to provide further information and analysis. They want to provide Image and text analysis capabilities to the applications which include identifying objects, people, text, scenes, and activities, and also provide highly accurate facial analysis and facial recognition. What service can provide this capability?
    1. Amazon Comprehend
    2. Amazon Rekognition
    3. Amazon Polly
    4. Amazon SageMaker
  4. A company wants to build generative AI applications using foundation models without managing infrastructure. Which service should they use?
    1. Amazon SageMaker
    2. Amazon Comprehend
    3. Amazon Bedrock
    4. Amazon Lex
  5. A development team wants an AI assistant that provides real-time code suggestions and security scanning in their IDE. Which service should they use?
    1. Amazon CodeGuru
    2. Amazon Q Developer (transitioning to Kiro)
    3. AWS Cloud9
    4. Amazon SageMaker
  6. A business analyst with no ML experience wants to build accurate ML models using a visual interface. Which service should they use?
    1. Amazon SageMaker Studio
    2. Amazon SageMaker Canvas
    3. Amazon Forecast
    4. Amazon Personalize
  7. A company needs to detect bias in their ML models and explain predictions for regulatory compliance. Which service should they use?
    1. Amazon SageMaker Ground Truth
    2. Amazon Inspector
    3. Amazon SageMaker Clarify
    4. AWS Audit Manager
  8. A company wants to train large foundation models for weeks with automated resiliency and checkpoint management. Which service should they use?
    1. Amazon SageMaker Training Jobs
    2. Amazon SageMaker HyperPod
    3. AWS Batch
    4. Amazon EC2 with GPU instances
  9. A contact center wants real-time insights from customer calls including sentiment analysis and agent assistance. Which service should they use?
    1. Amazon Transcribe
    2. Amazon Transcribe Call Analytics
    3. Amazon Comprehend
    4. Amazon Connect
  10. A company wants to build custom image recognition models to identify specific machine parts with minimal training data. Which service should they use?
    1. Amazon Rekognition (standard)
    2. Amazon Rekognition Custom Labels
    3. Amazon SageMaker
    4. Amazon Textract
  11. A company wants to deploy AI agents that can perform multi-step workflows, access enterprise tools, and maintain state across conversations in production. Which service should they use?
    1. Amazon Lex
    2. Amazon SageMaker AI
    3. Amazon Bedrock AgentCore
    4. AWS Step Functions
  12. A company needs Amazon’s own foundation models that offer industry-leading price-performance for text, image, and video generation tasks. Which model family should they use?
    1. Amazon Titan
    2. Amazon Nova
    3. Amazon Comprehend
    4. Amazon SageMaker JumpStart
  13. An enterprise wants a unified platform for data engineering, analytics, ML development, and generative AI that breaks down tool silos. Which service should they use?
    1. Amazon SageMaker AI
    2. Amazon EMR
    3. Amazon SageMaker Unified Studio
    4. AWS Glue
  14. A company wants to implement safety guardrails for their generative AI application to filter harmful content, block prompt injections, and protect sensitive information. Which service should they use?
    1. AWS WAF
    2. Amazon Macie
    3. Amazon Bedrock Guardrails
    4. AWS Shield

References