MLS-C01 Data Engineering Practice Question
A company is using AWS Glue ETL jobs to process data stored in Amazon S3. The jobs currently run sequentially and take too long. The data engineer wants to reduce job duration without rewriting the code. Which action is most effective?
⚠ Common exam trap
Watch out — candidates often confuse improving data format efficiency (Parquet) or incremental processing (job bookmarks) with solving a sequential execution bottleneck, when the direct solution is to increase parallelism via DPUs.
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 (Data Processing Units) for the job
Increasing the number of DPUs (Data Processing Units) for the AWS Glue ETL job directly allocates more distributed computing resources, enabling parallel execution of the job's stages. This reduces the overall runtime without requiring any code changes, as Glue automatically distributes the workload across the additional DPUs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the underlying EC2 instance type to a compute-optimized instance
Why it's wrong here
AWS Glue does not expose underlying EC2 instances; it uses DPUs.
- ✓
Increase the number of DPUs (Data Processing Units) for the job
Why this is correct
More DPUs allow parallel execution, reducing job duration.
- ✗
Convert the data from CSV to Parquet format
Why it's wrong here
Parquet can improve performance but requires code changes and does not inherently parallelize the job.
- ✗
Enable job bookmarks to skip already processed data
Why it's wrong here
Job bookmarks are for incremental processing, not for speeding up a full batch job.
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 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.