- A
The S3 bucket policy is too restrictive.
Why wrong: Restrictive policies cause access denied errors, not timeouts.
- B
The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.
Insufficient resources can cause timeouts.
- C
The Amazon Redshift cluster is in maintenance mode.
Why wrong: Maintenance mode would cause connection errors, not timeouts.
- D
The source data is not compressed.
Why wrong: Lack of compression affects performance but is unlikely to cause timeouts.
Question 883 of 1,755
Diagnosing AWS Glue Intermittent Timeouts Due to Insufficient DPUs
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
AWS Glue allocates DPUs as a unit of compute (1 DPU = 4 vCPU + 16 GB memory). For Spark-based jobs, the number of DPUs determines parallelism and shuffle capacity; under-allocation can cause tasks to spill to disk or fail due to memory pressure, manifesting as timeouts. The default timeout for Glue jobs is 48 hours, but internal service timeouts (e.g., 300 seconds for Spark executors) can occur if DPUs are insufficient for the data volume.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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