3.6 AI/ML and Analytics Services

AWS Certified Cloud Practitioner CLF-C02

Track your progress as you read

Finished sections get a check mark.

1 Introduction

About the AWS Cloud Practitioner (CLF-C02) Exam Study Strategies and Exam Tips

2 Domain 1: Cloud Concepts (24%)

1.1 What Is Cloud Computing? 1.2 Cloud Deployment Models 1.3 AWS Well-Architected Framework 1.4 Cloud Migration Strategies 1.5 Cloud Economics

3 Domain 2: Security and Compliance (30%)

2.1 The AWS Shared Responsibility Model 2.2 AWS Identity and Access Management (IAM) 2.3 AWS Security Services 2.4 Compliance and Governance 2.5 Network Security

4 Domain 3: Cloud Technology and Services (34%)

3.1 AWS Compute Services 3.2 AWS Storage Services 3.3 AWS Database Services 3.4 AWS Networking and Content Delivery 3.5 Management and Governance Services 3.6 AI/ML and Analytics Services 3.7 Application Integration and Other Services

5 Domain 4: Billing, Pricing, and Support (12%)

4.1 AWS Pricing Models 4.2 Billing and Cost Management Tools 4.3 AWS Support Plans 4.4 Additional Billing Concepts and Services

6 AWS Services Deep Dive

5.1 Compute Services Deep Dive 5.2 Storage and Database Deep Dive 5.3 Security Services Deep Dive 5.4 Architecture Best Practices and Design Principles 5.5 Practice Exam — Mixed Domain Questions

3.6 AI/ML and Analytics Services

Key Takeaways Explain This Topic AI

Amazon SageMaker AI (formerly Amazon SageMaker) is a fully managed service for building, training, and deploying machine learning models at scale.

Amazon Bedrock provides access to foundation models (Claude, Llama, Mistral) for building generative AI applications without managing infrastructure.

Amazon Rekognition uses deep learning for image and video analysis — identify objects, faces, text, and activities.

Amazon Athena is a serverless interactive query service that lets you analyze data in S3 using standard SQL.

Amazon Kinesis processes real-time streaming data from sources like IoT devices, application logs, and clickstreams.

Last updated: October 2026

Quick Answer: The CLF-C02 expects you to recognize each AI/ML and analytics service by a one-line purpose. SageMaker AI builds custom ML models; Bedrock provides foundation models for generative AI; pre-trained AI services (Rekognition, Transcribe, Polly, Translate, Comprehend, Lex, Textract) solve specific tasks with no ML expertise; and analytics services (Athena, Kinesis, EMR, Quick Sight, Glue) query, stream, process, visualize, and prepare data.

• Machine Learning Platform
• Amazon SageMaker AI (formerly Amazon SageMaker)

A fully managed ML service to build, train, tune, and deploy custom ML models at scale. AWS renamed it on December 3, 2024; the name Amazon SageMaker now covers a broader data, analytics, and AI platform that includes SageMaker AI.

• Feature Description
• SageMaker Studio Integrated development environment for ML
• Built-in Algorithms Pre-built algorithms for common tasks
• Training Managed training with automatic model tuning
• Deployment One-click deployment to production endpoints
• Ground Truth Data labeling combining humans and ML

Use when: an organization is building its own ML models and wants control over the full training-to-deployment pipeline.

Amazon Bedrock

A fully managed service for building generative AI applications using foundation models from leading providers — no infrastructure to manage.

• Feature Description
• Foundation Models Claude (Anthropic), Llama (Meta), Mistral, Cohere, Stable Diffusion, Amazon Nova/Titan
• Customization Fine-tune models with your own data
• Agents Build AI agents that take actions against your systems
• Guardrails Safety filters and responsible-AI controls
• Knowledge Bases Retrieval-Augmented Generation (RAG) over your data

Use when: building chatbots, content generation, or summarization on top of pre-trained foundation models rather than training your own.

Amazon Q

AWS's generative-AI assistant : Amazon Q Business answers questions over your company data, and Amazon Q Developer assists with code generation, debugging, and optimization (and helps in the AWS console).

AI Services (Pre-Trained — No ML Expertise Needed)

• Service What It Does Use Case
• Amazon Rekognition Image/video analysis Face detection, object recognition, content moderation
• Amazon Transcribe Speech to text Transcription, subtitles, call analytics
• Amazon Polly Text to speech Read content aloud, IVR, accessibility
• Amazon Translate Language translation Real-time translation, site localization
• Amazon Comprehend Natural language processing Sentiment analysis, entity/topic extraction
• Amazon Lex Conversational chatbots Virtual agents (powers Alexa)
• Amazon Textract Extract text/data from documents Invoice and form processing
• Amazon Kendra Intelligent enterprise search Search across documents and knowledge bases
• Amazon Personalize Real-time personalization Product/content recommendations

Amazon Forecast Time-series forecasting Demand and financial forecasting (no longer available to new customers)

On the Exam: Match the verb in the scenario to the service. "Add subtitles / transcribe" = Transcribe. "Read text aloud" = Polly. "Translate" = Translate. "Detect faces/objects" = Rekognition. "Build a chatbot" = Lex. "Extract data from invoices" = Textract. "Find sentiment/entities in text" = Comprehend.

• Analytics Services
• Amazon Athena

A serverless interactive query service that runs standard SQL directly on data in Amazon S3 — no servers, no loading. You pay per query based on data scanned, and it supports CSV, JSON, Parquet, and ORC. Great for ad-hoc analysis and log queries.

Amazon Kinesis

A real-time streaming platform:

• Service Purpose
• Kinesis Data Streams Collect and process streaming data
• Amazon Data Firehose (formerly Kinesis Data Firehose) Load streams into S3, Redshift, and more

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) Analyze streams with Apache Flink using Java, Scala, Python, or SQL

• Kinesis Video Streams Ingest and analyze video streams
• Amazon EMR (Elastic MapReduce)

A managed cluster platform for big-data frameworks like Apache Hadoop, Spark, and Presto at scale.

Amazon Quick Sight (formerly Amazon QuickSight)

A serverless business intelligence service for interactive dashboards and visualizations, now part of Amazon Quick.

AWS Glue

A serverless data integration / ETL service that discovers, prepares, and combines data for analytics. The Glue Data Catalog stores metadata, crawlers auto-discover schema, and ETL jobs transform data.

AWS Lake Formation

Simplifies building and securing a data lake on S3 with fine-grained permissions.

Analytics Quick Reference

• Need Service
• Query S3 data with SQL Athena
• Real-time streaming data Kinesis
• Big-data processing (Hadoop/Spark) EMR
• BI dashboards Quick Sight
• ETL / data preparation Glue
• Data warehouse queries Redshift
• Build a data lake Lake Formation

On the Exam: "SQL on S3, no loading" = Athena. "Real-time streams/IoT" = Kinesis. "Dashboards" = Quick Sight. "ETL/prepare data" = Glue. Do not confuse Athena (query) with Glue (transform).

Choosing Between AI Tiers

A frequent point of confusion is when to use a pre-trained AI service versus SageMaker AI versus Bedrock . Use a pre-trained service (Rekognition, Comprehend, and the rest) when AWS already solves your exact task and you want an API call with no ML skills required . Reach for SageMaker AI when you need a custom model trained on your own data with full control of the pipeline. Reach for Bedrock when you want generative AI — text generation, summarization, chat, or image creation — built on someone else's foundation model that you may lightly customize.

This "buy vs. build vs. generate" framing maps cleanly to exam scenarios: a company that "wants to add image moderation without data scientists" needs Rekognition; one that "needs to train a fraud model on its transaction history" needs SageMaker AI; one that "wants a chatbot using a large language model" needs Bedrock. Keeping these three tiers straight resolves most AI questions on the test.