DEA-C01 Data Ingestion and Transformation Practice Question
A company runs a data ingestion pipeline that uses AWS Glue to read 500 GB of JSON files from an S3 bucket (s3://raw-data/) every hour. The Glue ETL job transforms the data and writes Parquet files to another S3 bucket (s3://processed-data/). The job is triggered by a time-based CloudWatch Events rule. Recently, the job has started taking over 2 hours to complete, causing delays in downstream processes. The data volume has been consistent, and no changes have been made to the job code or infrastructure. The S3 bucket 's3://raw-data/' receives new files continuously, but the Glue job reads all files in the bucket each run (no incremental processing). The engineer suspects that the job is reprocessing old data. Which action should the engineer take FIRST to reduce the job duration?
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 Glue job bookmarking and configure the job to process only new data.
Enabling job bookmarking in Glue allows the job to process only new files since the last run, dramatically reducing processing time. Option D (increasing DPUs) would help with resource constraints but does not address the root cause of reprocessing old data. Option B (increasing parallelism) may help but not as much as eliminating reprocessing. Option C (using partition pruning) assumes the data is partitioned, but the stem says all files are read; partitioning might not help if files are not organized by time. The most impactful first step is to enable bookmarking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Glue job bookmarking and configure the job to process only new data.
Why this is correct
Bookmarking tracks processed files, so subsequent runs only process new files, drastically reducing runtime.
- ✗
Increase the parallelism of the Spark job by repartitioning the data.
Why it's wrong here
Repartitioning may improve parallelism but does not eliminate the need to process all files each time.
- ✗
Add partition pruning by modifying the S3 path to include date-based partitions.
Why it's wrong here
Partition pruning helps if data is partitioned, but the stem does not mention partitioned data; bookmarking is a more direct solution.
- ✗
Increase the number of DPUs for the Glue job to 100.
Why it's wrong here
While more DPUs can speed up processing, they do not prevent reprocessing old data; the job would still process all 500 GB, which is the root cause.
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 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.