- A
Switch the output to Amazon S3 instead of Redshift
Why wrong: Does not address the memory issue.
- B
Increase the number of DPUs allocated to the Glue job
More DPUs provide more memory.
- C
Reduce the number of partitions in the input data
Why wrong: Reducing partitions may reduce parallelism and increase memory per task, but could also cause OOM if data skew exists.
- D
Increase the spark.sql.shuffle.partitions parameter
Why wrong: May help but not the primary fix for OOM.
- E
Enable job metrics in CloudWatch to monitor memory usage
Monitoring helps diagnose the issue.
Question 313 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 read from Amazon S3 and write to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. Which TWO actions should the data engineer take to resolve this issue? (Choose TWO.)
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
Increasing the number of DPUs allocated to the Glue job (Option B) directly addresses the 'Out of Memory' errors by providing more memory and compute resources per executor. AWS Glue uses Apache Spark under the hood, where each DPU provides 4 vCPU and 16 GB of memory; adding more DPUs increases the total memory available for data processing, reducing the likelihood of OOM errors during shuffle or aggregation operations.
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.
- ✗
Switch the output to Amazon S3 instead of Redshift
Why it's wrong here
Does not address the memory issue.
- ✓
Increase the number of DPUs allocated to the Glue job
Why this is correct
More DPUs provide more memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of partitions in the input data
Why it's wrong here
Reducing partitions may reduce parallelism and increase memory per task, but could also cause OOM if data skew exists.
- ✗
Increase the spark.sql.shuffle.partitions parameter
Why it's wrong here
May help but not the primary fix for OOM.
- ✓
Enable job metrics in CloudWatch to monitor memory usage
Why this is correct
Monitoring helps diagnose the issue.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse increasing shuffle partitions (Option D) with a direct fix for OOM errors, when in fact it can increase memory pressure due to more concurrent tasks and metadata overhead, while the correct approach is to allocate more DPUs to scale memory and compute resources.
Detailed technical explanation
How to think about this question
AWS Glue jobs run on a managed Apache Spark environment where each DPU corresponds to a Spark executor with 4 vCPU and 16 GB of memory. When a job encounters 'Out of Memory' errors, it often indicates that the data being processed exceeds the memory available per executor, especially during shuffle operations or when using wide transformations like groupBy or join. Increasing DPUs not only adds more executors but also increases the total cluster memory, allowing Spark to better handle large datasets without spilling to disk or crashing.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of DPUs allocated to the Glue job — Increasing the number of DPUs allocated to the Glue job (Option B) directly addresses the 'Out of Memory' errors by providing more memory and compute resources per executor. AWS Glue uses Apache Spark under the hood, where each DPU provides 4 vCPU and 16 GB of memory; adding more DPUs increases the total memory available for data processing, reducing the likelihood of OOM errors during shuffle or aggregation operations.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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