Cloud Technology and Services
AI, Machine Learning, and Analytics Recognition
ImportantAssociate current in-scope AI/ML and analytics services with their primary tasks without drifting into model development or data-engineering implementation.
Aligned to the current CLF-C02 exam guide, verified August 15, 2026.
Why this matters
The exam tests whether a foundational candidate can recognize the service that matches a stated AI or analytics outcome.
Must Know
- Amazon SageMaker AI is a managed platform for building, training, and deploying machine-learning models. Amazon Lex builds conversational voice and text interfaces.
- Amazon Comprehend analyzes text; Amazon Polly converts text to speech; Amazon Transcribe converts speech to text; Amazon Translate translates text.
- Amazon Rekognition analyzes images and video, while Amazon Textract extracts text, handwriting, forms, and tables from documents.
- Amazon Athena runs serverless interactive SQL queries on data in Amazon S3. Amazon Kinesis handles streaming data. AWS Glue integrates and catalogs data. Amazon QuickSight provides business-intelligence analyses, dashboards, and visualizations.
- Amazon Redshift is a managed data warehouse; Amazon EMR runs big-data frameworks; Amazon OpenSearch Service supports search and analytics. These are recognition-level services here.
Compare and Distinguish
- SageMaker AI vs prebuilt AI services: SageMaker AI supports custom ML development; services such as Textract or Transcribe provide a defined capability.
- Lex vs Polly and Transcribe: Lex manages conversation and intent; Polly produces speech; Transcribe turns speech into text.
- Rekognition vs Textract: Rekognition analyzes visual content; Textract extracts structured content from documents.
- Athena vs Kinesis vs Glue vs Amazon Quick Sight: Athena queries, Kinesis streams, Glue integrates/catalogs, and Quick Sight visualizes.
- Athena vs Redshift: Athena queries data such as objects in S3 without managing servers; Redshift is a data warehouse.
Scenario examples
- Scenario: A company wants to build and deploy its own machine-learning model. Think: Amazon SageMaker AI.
- Scenario: A contact application needs a conversational chatbot. Think: Amazon Lex.
- Scenario: Analysts want to query data in S3 with SQL without provisioning a database. Think: Amazon Athena.
- Scenario: Executives need interactive business-intelligence dashboards. Think: Amazon QuickSight.
Exam traps
- Do not choose SageMaker AI for every AI requirement when a prebuilt service directly performs the stated task.
- Text-to-speech and speech-to-text are opposite directions.
- AWS Glue prepares, integrates, and catalogs data; it is not the business dashboard itself.
- Do not choose a query, streaming, catalog, or warehouse service when the requirement is an interactive business-intelligence dashboard.
Key takeaways
- Custom ML lifecycle: SageMaker AI. Conversation: Lex.
- Text insight: Comprehend. Speech out: Polly. Speech in: Transcribe. Documents: Textract. Images/video: Rekognition.
- Query: Athena. Stream: Kinesis. Integrate/catalog: Glue. Dashboard: Amazon Quick Sight.
How it works
- Prebuilt AI services accept a supported input modality and return a specialized result through managed APIs.
- Analytics services operate at different stages: ingest, integrate/catalog, query or warehouse, and visualize.
When to use it
- Use SageMaker AI for the custom ML lifecycle and a prebuilt AI service for a named capability.
- Use Athena for serverless SQL over S3, Kinesis for streams, Glue for integration/cataloging, and Amazon Quick Sight for BI dashboards.
- Recognize Redshift for data warehousing, EMR for big-data frameworks, and OpenSearch Service for search and analytics.
Security and governance implications
- AI and analytics services still require appropriate permissions, data protection, and governance of sensitive inputs and outputs.
- A service capability does not grant permission to use data for that purpose.
Operational context
- If two AI answers seem plausible, identify the input and requested output modality.
- If two analytics answers seem plausible, locate the exact stage: streaming, preparation/catalog, query, warehouse, or visualization.
Current AWS note
- The live guide renders the business-intelligence name as Amazon Quick Sight. Current product documentation places the QuickSight experience within Amazon Quick. Recognize the guide spelling and the current name as the same dashboard and visualization capability.
More detail
- Amazon Q is an in-scope generative AI assistant family. At Cloud Practitioner level, recognize it as AI assistance rather than memorizing product-specific administration.
- Analytics workflows can combine services: Kinesis can collect streaming events, Glue can prepare/catalog data, Athena can query S3, and Amazon Quick Sight can present findings. The exam can still ask for the one service matching a decisive step.
Ready for the quiz?
- Which service converts an audio recording into text?
- How does Textract differ from Rekognition?
- Which analytics verb distinguishes Kinesis from Athena?
- When is SageMaker AI more suitable than a prebuilt AI service?
Related objectives
- D3.7 — Identify AWS artificial intelligence and machine learning (AI/ML) services and analytics services.