DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer runs an AWS Glue job that reads a large partitioned Parquet dataset from Amazon S3 and writes aggregated results to another S3 prefix. The job runs daily and currently reprocesses the entire dataset each time, which is becoming expensive. The engineer wants subsequent runs to process only data added since the last successful run, based on the job's state. Which feature should the engineer enable?
⚠ Common exam trap
Watch out — candidates often confuse catalog performance features such as partition indexes with state tracking, when only job bookmarks persist which data a Glue job has already processed between runs.
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
✓
AWS Glue job bookmarks
AWS Glue job bookmarks maintain state across runs, recording which S3 objects, partitions, or JDBC rows were processed. When enabled, a subsequent run reads only new or modified data, avoiding full reprocessing. This is the built-in mechanism for incremental Glue jobs, unlike partition indexes, S3 Inventory, or retry settings, which do not track processed data across runs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue job bookmarks
Why this is correct
Job bookmarks persist state about which S3 objects or partitions have already been processed during prior runs. On subsequent runs, Glue reads only new or changed data, which reduces runtime and cost. This directly satisfies the requirement to process only data added since the last successful run, using the job's own state tracking.
- ✗
AWS Glue job retry with a smaller max retry count
Why it's wrong here
Job retries control what happens after a failure, not which data is read on a successful subsequent run. A lower retry count changes failure handling only. It does not introduce state tracking for incremental processing, so the job would continue to reprocess the entire dataset on each successful run.
- ✗
AWS Glue Data Catalog partition indexes
Why it's wrong here
Partition indexes accelerate metadata lookups and partition pruning in the Data Catalog for query engines. They do not track which S3 objects a Glue job has already processed, so they cannot restrict a job to only newly added data. Enabling indexes would improve catalog performance but leave the full-dataset reprocessing behavior unchanged.
- ✗
Amazon S3 Inventory reports on the source prefix
Why it's wrong here
S3 Inventory provides scheduled reports listing objects and their metadata. It can help audit what exists in a bucket, but it does not integrate with Glue job state to automatically limit reads to new data. The engineer would still need custom logic to compare inventory reports and filter inputs, which is not the requested built-in state-tracking feature.
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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