DEA-C01 Data Operations and Support Practice Question
A company is running a critical data pipeline using AWS Glue. The pipeline must be highly available and fault-tolerant. Which TWO strategies should the data engineer implement? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to confuse increasing concurrency (Option C) with fault tolerance, but concurrency only scales processing horizontally without providing redundancy against infrastructure failures.
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 Glue job to run in multiple Availability Zones.
Configuring the Glue job to run in multiple Availability Zones ensures that if one AZ experiences a failure, the job can continue processing in another AZ, providing high availability and fault tolerance. This is a fundamental strategy for resilient data pipeline design in AWS.
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 the Glue job to run in multiple Availability Zones.
Why this is correct
Multi-AZ provides redundancy.
- ✗
Use a single instance type for all Glue workers.
Why it's wrong here
Does not improve fault tolerance.
- ✗
Increase the number of concurrent runs for the Glue job.
Why it's wrong here
Concurrency does not guarantee fault tolerance.
- ✓
Enable job retries with exponential backoff.
Why this is correct
Retries handle transient failures.
- ✗
Disable job bookmarks to avoid reprocessing.
Why it's wrong here
Bookmarks help with incremental processing, not fault tolerance.
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