- A
Increase the number of Spark partitions using repartition(), but keep the same nodes.
Why wrong: May increase parallelism but not memory per task.
- B
Change the core node instance type to r5.xlarge (memory-optimized).
More memory per node helps OOM.
- C
Increase the number of executor cores in the Spark configuration.
Why wrong: Would increase parallelism but not memory per core.
- D
Enable Kryo serialization in the Spark configuration.
Why wrong: Reduces serialization overhead but not OOM.
Question 1,689 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 uses Amazon EMR to process large datasets stored in Amazon S3. The data is in Parquet format and partitioned by date. The EMR cluster uses Spark SQL for transformations. Recently, the job has been slow and some tasks are failing due to 'java.lang.OutOfMemoryError'. The cluster has 10 core nodes of type m5.xlarge. Which configuration change would MOST improve performance and stability?
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
✓
Change the core node instance type to r5.xlarge (memory-optimized).
The error 'java.lang.OutOfMemoryError' indicates that the Spark executors are running out of memory during processing. The m5.xlarge instance type provides 16 GiB of memory, but the workload likely requires more memory per task. Switching to r5.xlarge (32 GiB of memory) doubles the available memory per node, reducing memory pressure and preventing task failures, which directly improves stability and performance for memory-intensive transformations.
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.
- ✗
Increase the number of Spark partitions using repartition(), but keep the same nodes.
Why it's wrong here
May increase parallelism but not memory per task.
- ✓
Change the core node instance type to r5.xlarge (memory-optimized).
Why this is correct
More memory per node helps OOM.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of executor cores in the Spark configuration.
Why it's wrong here
Would increase parallelism but not memory per core.
- ✗
Enable Kryo serialization in the Spark configuration.
Why it's wrong here
Reduces serialization overhead but not OOM.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often focus on tuning Spark configurations (partitions, cores, serialization) to fix OutOfMemoryErrors, but the real issue is insufficient physical memory per node, which requires a change in instance family rather than software settings.
Detailed technical explanation
How to think about this question
Under the hood, Spark's memory management divides the executor heap into execution memory (for shuffles, joins, aggregations) and storage memory (for caching). When tasks exceed the available execution memory, Spark spills to disk, but if spill is insufficient or impossible, an OutOfMemoryError occurs. The r5.xlarge instance uses DDR4 memory with higher bandwidth and capacity, which directly alleviates this bottleneck. In real-world scenarios, memory-optimized instances are critical for workloads involving large Parquet files with wide schemas or complex aggregations, where memory per core is the limiting factor.
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.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Change the core node instance type to r5.xlarge (memory-optimized). — The error 'java.lang.OutOfMemoryError' indicates that the Spark executors are running out of memory during processing. The m5.xlarge instance type provides 16 GiB of memory, but the workload likely requires more memory per task. Switching to r5.xlarge (32 GiB of memory) doubles the available memory per node, reducing memory pressure and preventing task failures, which directly improves stability and performance for memory-intensive transformations.
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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