DEA-C01 Data Operations and Support Practice Question
A data engineer maintains an AWS Glue ETL job that reads from an Amazon Kinesis Data Streams stream and writes to Amazon S3 in Parquet format. The job has been running successfully, but after the data volume increased threefold, the job now fails with an error stating that the Glue job's bookmarks are not advancing and the job is reprocessing old data. The engineer has enabled job bookmarks with the default settings. Which action should the engineer take to resolve the issue?
⚠ Common exam trap
The trap here is assuming that scaling resources or changing the output format will fix bookmark issues, when the root cause is usually a transformation that breaks bookmark propagation.
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
✓
Verify that the Kinesis Data Streams source is configured with the correct stream name and that the job's transformation does not include an unsupported operation that blocks bookmark propagation, such as a custom transformation that does not preserve the bookmark state.
Job bookmarks in AWS Glue track the last processed data to avoid reprocessing. When bookmarks fail to advance, it is typically due to an unsupported transformation or a misconfigured source. Ensuring the Kinesis source is correctly configured and that all transformations preserve bookmark state will allow the job to process only new data and advance the bookmark.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable job bookmarks and instead use an AWS Lambda function to track the last processed Kinesis sequence number in Amazon DynamoDB.
Why it's wrong here
Disabling job bookmarks and implementing a custom tracking solution adds complexity and operational overhead. While it could work, it is not the recommended approach when the built-in bookmark feature is designed to handle this scenario. The issue is more likely a misconfiguration that can be fixed without abandoning bookmarks.
- ✓
Verify that the Kinesis Data Streams source is configured with the correct stream name and that the job's transformation does not include an unsupported operation that blocks bookmark propagation, such as a custom transformation that does not preserve the bookmark state.
Why this is correct
Job bookmarks rely on the source and transformations to propagate state. If a transformation, such as a custom code node or a filter that changes the record order, does not properly pass along the bookmark, the job will reprocess data. Ensuring the source configuration is correct and that transformations are bookmark-compatible is essential to resolve the issue.
- ✗
Increase the number of AWS Glue DPUs allocated to the job and enable auto-scaling.
Why it's wrong here
Adding more DPUs increases compute capacity and may improve throughput, but it does not address the root cause of bookmarks not advancing. The issue is likely due to a transformation that breaks bookmark propagation, such as an unsupported operation or a change in the job's source configuration. Simply scaling up will not fix the bookmark state.
- ✗
Change the output format from Parquet to JSON, as Parquet does not support job bookmarks with Kinesis sources.
Why it's wrong here
The output format does not affect job bookmark functionality. Parquet is a supported and recommended format for AWS Glue jobs, including those reading from Kinesis. The claim that Parquet does not support bookmarks is incorrect; bookmarks operate at the source and transformation level, not the sink format.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.