MLS-C01 Data Engineering Practice Question
A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with timeouts. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse intermittent failures with configuration issues (like policies or maintenance) rather than recognizing that resource starvation (insufficient DPUs) is the classic cause of sporadic timeouts in distributed ETL jobs.
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
✓
The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.
Intermittent timeouts in AWS Glue ETL jobs typically indicate insufficient resource allocation. DPUs (Data Processing Units) define the compute capacity for the job; if too few are allocated, the job may run slowly and exceed the default timeout (e.g., 2880 minutes) or internal service limits, especially when processing large datasets from S3 to Redshift. Increasing the DPU count or using the G.1X/G.2X worker types can resolve this.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The S3 bucket policy is too restrictive.
Why it's wrong here
Restrictive policies cause access denied errors, not timeouts.
- ✓
The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.
Why this is correct
Insufficient resources can cause timeouts.
- ✗
The Amazon Redshift cluster is in maintenance mode.
Why it's wrong here
Maintenance mode would cause connection errors, not timeouts.
- ✗
The source data is not compressed.
Why it's wrong here
Lack of compression affects performance but is unlikely to cause timeouts.
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
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