DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to transform data from Amazon S3 and load it into Amazon Redshift. The job needs to run daily and process only new data since the last run. The data in S3 is partitioned by date in the format `year=YYYY/month=MM/day=DD/`. Which feature should the data engineer use to track processed partitions and avoid reprocessing old data?
⚠ Common exam trap
Many exam-takers confuse scheduling or catalog maintenance features with state management, assuming that a trigger or crawler schedule would automatically handle incremental processing.
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 are designed to track the state of data processed by a job. When reading from partitioned S3 data, bookmarks record the last processed partition and only process new partitions in subsequent runs. This enables incremental processing without manual intervention, making it the correct choice for avoiding reprocessing of old data.
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
AWS Glue job bookmarks track the data that has already been processed in previous job runs. When reading from S3 with partitions, bookmarks keep track of the last processed partition and only process new partitions in subsequent runs. This is the recommended way to handle incremental processing without manual tracking.
- ✗
AWS Glue crawler update schedules
Why it's wrong here
Crawler schedules update the Data Catalog with new partitions, but they do not inform the ETL job which partitions have been processed. The job would still need a mechanism like bookmarks to avoid reprocessing. Crawlers only maintain metadata, not job state.
- ✗
AWS Glue triggers with a schedule
Why it's wrong here
Triggers schedule the job to run at specific times, but they do not track which data has been processed. Without bookmarks, the job would reprocess all data each run, leading to duplicates and inefficiency. Triggers only control when the job runs, not what data it processes.
- ✗
AWS Glue Data Catalog partition indexes
Why it's wrong here
Partition indexes improve query performance in the Data Catalog, but they do not track processed data for ETL jobs. They are used by services like Amazon Athena to speed up partition pruning. They do not provide state management for 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 |
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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
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.