Identify AWS Artificial Intelligence and Machine Learning (AI/ML) Services And Analytics Services

📊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).

Additional Resources

1. Analytics
2. Machine Learning (ML) and Artificial Intelligence (AI)