📊Exam Guide: Cloud Practitioner
Domain 3: Cloud Technology & Services
📘Task Statement 3.7
🎯 What Is This Task Testing?
You need to recognize common AWS services for:
- AI/ML and what each service is used for (SageMaker, Lex, Kendra)
- Analytics and when to use each service (Athena, Kinesis, Glue, QuickSight)
1) 🤖 AWS AI/ML Services
Amazon SageMaker
A managed service to build, train, and deploy machine learning models.
Use Amazon Sagemaker When:
- you need an end-to-end ML platform (data prep, training, tuning, deployment)
- you want to manage ML workflows without building all tooling yourself
“train a model,” “deploy an ML model,” “ML lifecycle” → SageMaker.
Amazon Lex
A service for building chatbots and conversational interfaces (text and voice).
Use Amazon Lex When:
- you want a chatbot for customer support, internal help desk, or booking flows
- you need natural language understanding for conversation-style interfaces
“chatbot,” “conversational interface,” “voice/text bot” → Lex.
Amazon Kendra
An intelligent search service for searching across large volumes of content (documents, knowledge bases).
Use Amazon Kendra When:
- you want enterprise search across documents and internal data sources
- you need more “meaning-based” search than basic keyword matching
“search documents/knowledge base,” “enterprise search” → Kendra.
2) 🗺️ AWS Analytics Services
Ingestion → ETL → Query → Visualization
A helpful way to remember analytics services is by the stage they support.
Amazon Kinesis: Streaming Ingestion/Processing
A platform for real-time streaming data.
Use Amazon Kinesis When:
- you need to ingest or process data continuously (clickstreams, IoT telemetry, logs)
- you need near-real-time analytics
“real-time streams,” “ingest streaming data” → Kinesis.
AWS Glue: ETL and Data Integration
A managed service for ETL (extract, transform, load) and data preparation.
Use AWS Glue When:
- you need to clean/transform and move data between sources and targets
- you need a managed data integration/ETL service
“ETL,” “transform data,” “prepare data for analytics” → Glue.
Amazon Athena: Query Data in S3 Using SQL
A serverless query service that lets you analyze data in Amazon S3 using SQL.
Use Amazon Athena When:
- you want ad-hoc queries without managing servers
- your data is already in S3 and you want SQL-based analysis
“query S3 with SQL,” “serverless interactive queries” → Athena.
Amazon QuickSight: Visualization / BI
A business intelligence service for dashboards and data visualization.
Use Amazon QuickSight When:
- you want interactive dashboards and reporting for stakeholders
- you need BI-style visual analytics
“dashboards,” “visualize data,” “BI reporting” → QuickSight.
“Match the Service”
- “Build/train/deploy ML models” → SageMaker
- “Create a chatbot” → Lex
- “Search across documents/knowledge bases” → Kendra
- “Ingest streaming data in real time” → Kinesis
- “ETL / data preparation” → Glue
- “Run SQL queries directly on S3” → Athena
- “Build dashboards and visual reports” → QuickSight
✅ Quick Exam-Style Summary
- AI/ML: SageMaker (ML platform), Lex (chatbots), Kendra (intelligent search).
- Analytics: Kinesis (streaming), Glue (ETL), Athena (SQL on S3), QuickSight (dashboards).
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