DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is configuring an AWS Glue job bookmark on a job that reads partitioned Parquet data from Amazon S3 and writes to another S3 location. The engineer notices that reprocessing keeps occurring and wants the bookmark to correctly skip already-processed data. Which two actions should the engineer take? (Choose two.)
⚠ Common exam trap
The trap here is treating job bookmarks as automatic, when they must be explicitly enabled and only work against supported sources with intact state.
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
✓
Ensure the job reads from a source that supports bookmarks, such as S3 or the Glue Data Catalog, rather than an unsupported source
Reprocessing under a bookmark usually means the feature is not actually tracking the source. The bookmark must be enabled on the job and the source must be a supported type such as S3 or the Glue Data Catalog, with no manual reset in between. Confirming both restores incremental processing so already-consumed partitions are skipped.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure the job reads from a source that supports bookmarks, such as S3 or the Glue Data Catalog, rather than an unsupported source
Why this is correct
Job bookmarks only track state for supported sources, primarily Amazon S3 and JDBC/Glue Data Catalog tables. If the transform reads from an unsupported origin, Glue cannot persist progress and the job reprocesses everything each run. Confirming the source is a bookmark-capable type is a prerequisite for the feature to function at all in this pipeline.
- ✗
Increase the number of worker nodes so the bookmark commits faster between runs
Why it's wrong here
Bookmark state is committed by the job run itself, not by worker count. Adding workers raises parallelism for the transformation but does not change whether previously processed partitions are skipped. The reprocessing symptom would remain, because the root cause is bookmark configuration or state, not compute capacity.
- ✗
Enable the Glue Data Catalog schema evolution setting on the job
Why it's wrong here
Schema evolution controls how new or changed columns are reflected in catalog metadata; it has no bearing on which S3 objects a job has already consumed. Enabling it would not stop reprocessing and could even introduce new columns unexpectedly. It is unrelated to bookmark state tracking for partitioned Parquet inputs.
- ✓
Set the job's bookmark option to enable and confirm no manual state reset was performed between runs
Why this is correct
The bookmark must be explicitly enabled on the job, and any reset of bookmark state forces a full reprocess on the next run. Verifying the option is set to enable and that no reset occurred ensures Glue resumes from the last committed position instead of re-reading the entire partitioned dataset. This directly addresses the repeated reprocessing symptom.
- ✗
Convert the output to a single unpartitioned file so the bookmark can track it
Why it's wrong here
Bookmarks track input objects and partitions, not output layout. Collapsing output to one file neither improves tracking nor is required, and it would hurt downstream read performance on a partitioned dataset. This misdiagnoses the cause and changes the output design without fixing skipped-input behavior.
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 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
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