DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to transform data in Amazon S3. The data is partitioned by date and hour. The job reads the latest hour's data, performs aggregations, and writes results to a separate S3 bucket. The job runs every hour and processes approximately 500 MB of input data. The team notices that the job takes longer than expected, often exceeding the 1-hour window. Which action would most effectively reduce the job's runtime?
⚠ Common exam trap
Many exam-takers confuse 'repartitioning' (Option C) with 'increasing parallelism' — but without more workers, more partitions simply create scheduling overhead and do not reduce runtime.
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 workers (DPUs) for the Glue job.
Increasing the number of workers (DPUs) for the Glue job directly addresses the root cause: the job is CPU- or memory-bound due to insufficient parallelism for the 500 MB hourly workload. By allocating more DPUs, AWS Glue can distribute the aggregation and write operations across more executors, reducing wall-clock time and keeping the job within the 1-hour window. This is the most effective action because the job's bottleneck is compute capacity, not data format or processing framework.
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 Python shell job instead of a Spark job.
Why it's wrong here
Python shell jobs are for light processing and not suitable for large transformations.
- ✗
Switch from using DynamicFrame to using Spark SQL for transformations.
Why it's wrong here
Spark SQL may have similar performance; the bottleneck is likely resources, not API.
- ✗
Repartition the input data into more partitions before reading.
Why it's wrong here
Repartitioning adds overhead and does not help when reading a single partition.
- ✓
Increase the number of workers (DPUs) for the Glue job.
Why this is correct
More workers increase parallelism, reducing runtime for the given data size.
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.