DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to schedule an AWS Glue ETL job to run every hour and process new data that arrives in an S3 bucket. The job should only process files that have been added since the last run. Which approach should the engineer use to track which files have been processed?
⚠ Common exam trap
A common mix-up: candidates choose Option A (S3 Event Notifications) because it seems like a direct trigger for new files, but they overlook that the requirement is for an hourly batch job that tracks processed files across runs, not a per-file event-driven trigger.
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
✓
Enable job bookmarks in the Glue ETL job.
AWS Glue job bookmarks automatically track the last processed data, allowing the job to incrementally process only new files since the last run. This is the native, built-in mechanism for stateful incremental processing in Glue ETL, eliminating the need for external tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure S3 Event Notifications to trigger the Glue job on each new object creation.
Why it's wrong here
Event notifications fire per object, giving no hourly schedule and no record of what a previous run consumed, so reruns duplicate data. It is tempting because notifications suit near-real-time, low-latency ingestion, and would be correct if the requirement were immediate processing rather than hourly batch tracking.
- ✓
Enable job bookmarks in the Glue ETL job.
Why this is correct
Job bookmarks persist state across runs, letting Glue track previously processed S3 objects by path and last-modified timestamp. This satisfies the hourly incremental requirement: each run reads only files added since the prior execution, avoiding full reprocessing. No external tracking table or custom filtering logic is needed.
- ✗
Store the last processed timestamp in a DynamoDB table and query it at the start of the job.
Why it's wrong here
A DynamoDB timestamp requires the job to filter S3 listings itself and risks reprocessing or missing late files; Glue job bookmarks track processed files automatically per run. It is tempting for custom state control, but bookmarks are the native mechanism for incremental S3 processing.
- ✗
Use S3 Inventory to list all objects and filter by last modified date in the job.
Why it's wrong here
S3 Inventory delivers a periodic manifest, often daily, so it cannot identify files added within the last hour and lags the schedule. It is tempting because inventory suits auditing and reconciling large buckets, and would be correct for bulk reporting rather than hourly incremental processing.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.