- A
Reduce the number of shuffle partitions.
Why wrong: Reducing shuffle partitions can cause memory issues; increasing may help with data skew.
- B
Enable dynamic allocation of executors.
Dynamic allocation allows Spark to scale resources based on workload.
- C
Disable speculative execution to reduce redundant tasks.
Why wrong: Disabling speculation can sometimes help, but it is not universally beneficial.
- D
Use a larger instance type for core nodes.
Larger instances provide more memory and CPU, improving performance.
- E
Use EMRFS consistent view to ensure data consistency.
Consistent view avoids errors from eventual consistency.
Question 969 of 1,786
Three Ways to Improve Spark Job Performance on Amazon EMR
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 running a 10-node Amazon EMR cluster to process data from Amazon S3. The cluster is using Apache Spark for transformations. The data processing is taking longer than expected. Which THREE actions can improve the performance of the Spark jobs on EMR? (Choose THREE.)
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
✓
Enable dynamic allocation of executors.
Option B is correct because enabling dynamic allocation of executors allows Amazon EMR to automatically scale the number of executors up or down based on workload demand. This prevents resource underutilization or over-provisioning, which can significantly improve Spark job performance by ensuring that the cluster's resources are efficiently matched to the processing needs of the 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.
- ✗
Reduce the number of shuffle partitions.
Why it's wrong here
Reducing shuffle partitions can cause memory issues; increasing may help with data skew.
- ✓
Enable dynamic allocation of executors.
Why this is correct
Dynamic allocation allows Spark to scale resources based on workload.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable speculative execution to reduce redundant tasks.
Why it's wrong here
Disabling speculation can sometimes help, but it is not universally beneficial.
- ✓
Use a larger instance type for core nodes.
Why this is correct
Larger instances provide more memory and CPU, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use EMRFS consistent view to ensure data consistency.
Why this is correct
Consistent view avoids errors from eventual consistency.
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 'reducing shuffle partitions' (Option A) as a universal performance fix, when in fact it can degrade performance due to data skew and memory issues, while the correct answer focuses on resource elasticity through dynamic allocation.
Detailed technical explanation
How to think about this question
Dynamic allocation works by having the Spark driver request executors from the cluster manager (YARN or EMR's built-in scheduler) based on the number of pending tasks, using parameters like spark.dynamicAllocation.minExecutors and spark.dynamicAllocation.maxExecutors. Under the hood, Spark periodically evaluates the backlog of tasks and scales executors accordingly, which is especially beneficial for EMR clusters where workloads can vary during different stages of a Spark job. In real-world scenarios, a batch processing job with multiple stages may see a 30-50% reduction in runtime when dynamic allocation is properly configured.
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.
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Data Ingestion and Transformation — study guide chapter
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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: Enable dynamic allocation of executors. — Option B is correct because enabling dynamic allocation of executors allows Amazon EMR to automatically scale the number of executors up or down based on workload demand. This prevents resource underutilization or over-provisioning, which can significantly improve Spark job performance by ensuring that the cluster's resources are efficiently matched to the processing needs of the 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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