MLS-C01 Data Engineering Practice Question
A company is building a data pipeline using AWS Glue to transform data from Amazon RDS to Amazon S3. The pipeline runs daily and processes about 500 GB of data. The team notices that the job is taking longer than expected. Which change would MOST improve the job performance?
⚠ Common exam trap
Many candidates confuse increasing DPUs with simply adding more memory, when in fact it scales both CPU and memory, and they may incorrectly assume that optimizing output file sizes or disabling bookmarks is a performance fix, whereas those changes address different concerns like cost or 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
✓
Increase the number of DPUs for the Glue job
Increasing the number of DPUs (Data Processing Units) for the AWS Glue job directly allocates more distributed computing resources, allowing the job to process the 500 GB dataset in parallel across multiple workers. This is the most effective way to reduce runtime for a large-scale ETL job, as Glue's Spark-based execution scales horizontally with DPU count.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable job bookmarking
Why it's wrong here
Job bookmarking helps incremental processing, disabling it may reprocess all data.
- ✓
Increase the number of DPUs for the Glue job
Why this is correct
More DPUs provide more parallelism and can speed up the job.
- ✗
Upgrade the RDS instance to a larger class
Why it's wrong here
The bottleneck is likely the Glue job, not the source database.
- ✗
Use smaller file sizes in S3 output
Why it's wrong here
Smaller files can increase metadata overhead and slow down 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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.