MLS-C01 Data Engineering Practice Question
A data engineering team uses AWS Glue to run ETL jobs. They notice that jobs are taking longer to complete as data volume grows. They want to optimize performance without increasing cost significantly. Which THREE strategies should they consider?
⚠ Common exam trap
A common mix-up: candidates assume adding more DPUs (E) is the only way to speed up Glue jobs, overlooking that data optimization strategies (partitioning and columnar formats) can yield similar or better performance gains without increasing cost.
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
✓
Partition the input data in S3
Partitioning input data in S3 (B) allows AWS Glue to use partition pruning, reading only the relevant subsets of data instead of scanning the entire dataset. This reduces I/O and processing time, directly addressing the performance degradation caused by growing data volumes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove partitioning from the output
Why it's wrong here
Would worsen performance.
- ✓
Partition the input data in S3
Why this is correct
Enables parallel processing.
- ✗
Use Amazon EMR instead of Glue
Why it's wrong here
Would increase management overhead and cost.
- ✓
Convert input data to columnar format (e.g., Parquet)
Why this is correct
Reduces data scanned and improves compression.
- ✓
Increase the number of DPUs (workers)
Why this is correct
More parallelism reduces job duration.
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.