DEA-C01 Data Ingestion and Transformation Practice Question
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.)
⚠ Common exam trap
Test-takers frequently 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.
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.
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.
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.
- ✗
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.
- ✓
Use EMRFS consistent view to ensure data consistency.
Why this is correct
Consistent view avoids errors from eventual consistency.
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
This DEA-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 DEA-C01 exam.