DEA-C01 Data Operations and Support Practice Question
A data engineering team notices that an AWS Glue ETL job, which processes hourly data from an S3 bucket, is taking progressively longer to run. The job reads Parquet files partitioned by date and hour. Which action is MOST likely to improve the job's performance?
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 pushdown predicate filtering on the job's data source.
Enabling pushdown predicate filtering allows the Glue job to read only the relevant partitions (e.g., specific date and hour) instead of scanning all data. This directly addresses the symptom of progressively longer run times as data accumulates. Option B is incorrect because Parquet is a columnar format optimized for performance, while CSV would increase I/O. Option C, increasing DPUs, can improve parallelism but does not reduce the amount of data read, so it may not address the root cause. Option D, switching to Python shell, would lose Spark's distributed processing capabilities and is unlikely to improve performance.
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 pushdown predicate filtering on the job's data source.
Why this is correct
Pushdown predicates filter data at the source, reducing data scanned.
- ✗
Convert Parquet files to CSV to improve read performance.
Why it's wrong here
Parquet is columnar and optimized for analytics; CSV would degrade performance.
- ✗
Increase the number of DPUs for the job.
Why it's wrong here
Adding DPUs helps but does not address the root cause of reading unnecessary partitions.
- ✗
Switch from Spark to Python shell for simpler processing.
Why it's wrong here
Python shell does not support distributed processing and would be slower.
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.