DEA-C01 Data Operations and Support Practice Question
A data engineer is designing a data pipeline using AWS Step Functions to orchestrate multiple AWS Glue ETL jobs. The pipeline must handle failures and retries. Which TWO configurations should the engineer use to ensure the pipeline is resilient? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the state machine to use a 'Catch' rule to handle specific errors and transition to a fallback state
To ensure resilience in AWS Step Functions, the engineer should configure a Catch rule to handle errors by transitioning to a fallback state (Option B) and define a Timeout for each state to prevent the pipeline from hanging indefinitely (Option D). A dead-letter queue (DLQ) is not directly used by Step Functions but by services like Lambda. A fixed retry interval is less effective than exponential backoff. Using a Parallel state is for concurrency, not resilience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a dead-letter queue (DLQ) for the state machine
Why it's wrong here
DLQ is not a feature of Step Functions.
- ✓
Configure the state machine to use a 'Catch' rule to handle specific errors and transition to a fallback state
Why this is correct
Catch rules handle errors gracefully.
- ✗
Set the 'Retry' interval to a fixed value instead of exponential backoff
Why it's wrong here
Exponential backoff is more resilient.
- ✓
Define a 'Timeout' for each state to prevent the pipeline from hanging indefinitely
Why this is correct
Timeout prevents stuck executions.
- ✗
Use a 'Parallel' state to run multiple Glue jobs simultaneously
Why it's wrong here
Parallel state does not handle failures.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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