DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to transform data in S3. The transformation job reads Parquet files, filters rows, and writes to another S3 bucket. The job takes longer than expected. Which change would MOST likely reduce the job execution time?
⚠ Common exam trap
Many exam-takers assume optimizing file format or output partitioning will always improve performance, but for a compute-bound transformation job, increasing parallelism via DPUs is the most direct solution.
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
✓
Increase the number of DPUs allocated to the Glue job.
Increasing the number of DPUs (Data Processing Units) allocated to the Glue job directly increases the parallelism of the Apache Spark-based execution environment. Since the job reads Parquet files, filters rows, and writes output, a bottleneck in compute capacity is the most likely cause of prolonged execution time. More DPUs allow Spark to distribute the workload across more executors, reducing overall runtime.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a single large file instead of multiple small files.
Why it's wrong here
Multiple small files allow better parallelism than a single large file.
- ✗
Reduce the number of partitions in the output data.
Why it's wrong here
Fewer partitions reduce parallelism.
- ✗
Convert the input files from Parquet to CSV format.
Why it's wrong here
CSV is less efficient than Parquet for analytical queries.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs allow more parallel processing, reducing runtime.
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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