Study CLF-C02 Analytics, AI/ML, and Integration: key concepts, common traps, and exam decision cues.
This is the part of CLF-C02 where AWS starts throwing many managed service names at you. The exam still stays broad. It mainly wants you to know which service family fits the job: analytics, messaging, AI/ML, customer engagement, developer tooling, end-user computing, frontend, or IoT.
| Need | Strongest first fit |
|---|---|
| Query data in S3 or do analytics work | Amazon Athena, AWS Glue, Amazon Kinesis, Amazon QuickSight |
| Build AI/ML solutions or use language/voice/search features | Amazon SageMaker, Lex, Kendra, Polly, Rekognition |
| Queue, fan out, or route events | Amazon SQS, Amazon SNS, Amazon EventBridge |
| Build and deploy software | AWS Cloud9, CodeBuild, CodePipeline, X-Ray |
| Deliver desktops or app streams to users | Amazon WorkSpaces, Amazon AppStream 2.0 |
| Build frontend web or mobile apps | AWS Amplify, AWS AppSync |
| Connect devices and IoT systems | AWS IoT Core, AWS IoT Greengrass |
These three often blur together:
| Service | Better mental model |
|---|---|
| Amazon SQS | queue work for later processing |
| Amazon SNS | publish notifications or fan out messages |
| Amazon EventBridge | route events between services and systems |
If the question is about asynchronous queued work, SQS is usually stronger than SNS. If it is about broadcasting notifications, SNS is often stronger. If it is about event routing across systems, EventBridge is usually the better lane.
You do not need model-training depth. You do need to know that AWS has:
If a stem is clearly about business intelligence or visualization, QuickSight is stronger than a storage service. If it is about conversational bots, Lex is a better fit than a generic compute answer.
CLF-C02 includes these because AWS wants broad platform literacy:
This domain gets easier if you classify the job first: