MLS-C01 Data Engineering Practice Question
A company is using AWS Glue to run ETL jobs that transform data from multiple sources into a data lake on S3. The jobs are scheduled to run hourly. Recently, the jobs have been failing intermittently with 'MemoryError' exceptions. The data volume has grown over time. The data engineer needs to resolve this issue cost-effectively. Which action should be taken?
⚠ Common exam trap
Candidates often confuse memory errors with data skew or partitioning issues, leading them to choose repartitioning (Option D) instead of recognizing that the root cause is insufficient total memory for the growing dataset.
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 and use a larger worker type.
The 'MemoryError' exception indicates that the Glue job is running out of memory as data volume grows. Increasing the number of DPUs (Data Processing Units) and using a larger worker type (e.g., from Standard to G.1X or G.2X) provides more memory and compute capacity per worker, allowing the job to handle larger datasets without failing. This is the most cost-effective approach because it scales resources only as needed, avoiding over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of DPUs allocated to the Glue job and use a larger worker type.
Why this is correct
More DPUs and larger worker types provide more memory to handle larger data volumes.
- ✗
Increase the S3 timeout settings in the Glue job configuration.
Why it's wrong here
Timeout settings do not affect memory allocation.
- ✗
Switch the Glue job type from Spark to Python shell to reduce memory overhead.
Why it's wrong here
Python shell uses less memory and will likely fail on large datasets.
- ✗
Repartition the data using Spark's repartition method before processing.
Why it's wrong here
Repartitioning may help but may not resolve memory errors if the overall memory is insufficient.
Visual reference
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.