DEA-C01 Data Operations and Support Practice Question
A data engineer maintains an AWS Glue job that reads JSON files from Amazon S3, applies a transform, and writes Parquet to a second bucket. The job's bookmark was enabled at creation, but each nightly run reprocesses all previously handled files, and downstream tables now contain duplicate rows. The job script has not been modified and the S3 prefix is unchanged. Which action will MOST directly resolve the duplicate processing?
⚠ Common exam trap
The trap here is assuming duplicated output always means the transformation is non-deterministic, when the usual cause is bookmark state that cannot be matched to a source or sink.
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
✓
Confirm the transformation_ctx parameter is passed to each source and sink call, then reset the job bookmark and rerun once to rebuild state.
AWS Glue job bookmarks persist per-source and per-sink state keyed by the transformation_ctx argument. If that context is missing or inconsistent, the job cannot determine which objects were already processed and re-reads the entire prefix, producing duplicates. Passing a stable transformation_ctx and resetting the bookmark to rebuild the state store fixes the incremental behavior without changing the transform logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reprocess the prefix with a job that has job bookmarks disabled and rely on the S3 object LastModified timestamp to filter files.
Why it's wrong here
Disabling bookmarks removes the very mechanism that tracks processed objects, guaranteeing full rescans. Filtering on LastModified is fragile because late-arriving or backfilled files with old timestamps would be skipped, while any file touched by metadata operations could be re-ingested. This approach trades duplicate rows for silently missing data.
- ✓
Confirm the transformation_ctx parameter is passed to each source and sink call, then reset the job bookmark and rerun once to rebuild state.
Why this is correct
Job bookmarks rely on the transformation_ctx value to namespace state per source and sink; when it is absent or has changed between runs, Glue cannot correlate prior state and falls back to reading everything. Supplying a stable transformation_ctx on the S3 source and sink, then resetting the bookmark to clear stale state, restores correct incremental processing.
- ✗
Increase the number of AWS Glue DPUs allocated to the job so the run completes before the next scheduled trigger.
Why it's wrong here
Worker capacity affects runtime and shuffle performance, not which input objects are considered new. Because the state store is not tracking processed objects, adding DPUs still lets the job read every file in the prefix on each run, so duplicates continue to be written. DPU sizing is the wrong lever for a bookmarking or state-tracking defect.
- ✗
Change the job's output write mode to append and add a deduplication step that drops rows whose keys already exist.
Why it's wrong here
Appending and then deduplicating masks the symptom while leaving the root cause in place. The job would still rescan the entire prefix every night, consuming unnecessary DPU-hours and growing runtime as the dataset expands. Deduplication also cannot reliably distinguish legitimate repeated business events from re-read files, so it risks discarding valid records.
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.