DEA-C01 Data Operations and Support Practice Question
An AWS Glue job that processes streaming data from Amazon Kinesis Data Streams is failing intermittently with 'Failed to checkpoint' errors. The job uses checkpointing to an Amazon S3 bucket every 60 seconds. Which action should the engineer take to resolve the issue?
⚠ Common exam trap
Many candidates assume DynamoDB is always faster for checkpoints (Option B), but AWS Glue streaming jobs natively support only S3 for checkpointing, and DynamoDB is not a valid checkpoint location—this distracts from the simple fix of adjusting the interval.
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
✓
Increase the checkpoint interval to 120 seconds.
The 'Failed to checkpoint' error in AWS Glue streaming jobs typically occurs when the checkpoint operation exceeds the 60-second interval due to high throughput or large state size. Increasing the checkpoint interval to 120 seconds provides more time for the checkpoint to complete, reducing the likelihood of timeouts and allowing the job to stabilize without losing progress.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the checkpoint interval to 120 seconds.
Why this is correct
Reduces the frequency of checkpoint writes, mitigating contention.
- ✗
Move the checkpoint location to an Amazon DynamoDB table.
Why it's wrong here
Glue streaming does not support DynamoDB for checkpoints.
- ✗
Decrease the Kinesis shard count to reduce throughput.
Why it's wrong here
Reducing shards could increase latency but not resolve checkpoint errors.
- ✗
Disable checkpointing and rely on Kinesis iterator age.
Why it's wrong here
Disabling checkpointing would cause data loss on failure.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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