DEA-C01 Data Operations and Support Practice Question
A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?
⚠ Common exam trap
DEA-C01 often tests the confusion between driver and executor memory, and the misconception that increasing parallelism or shuffle partitions directly solves heap memory errors.
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 spark.executor.memory
The OutOfMemoryError: Java heap space in Spark executors indicates that the executor JVM heap is insufficient to hold the data being processed. Increasing spark.executor.memory allocates more heap space to each executor, allowing it to handle larger partitions or aggregations without running out of memory. This directly addresses the root cause of the error.
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 spark.default.parallelism
Why it's wrong here
spark.default.parallelism sets the default number of partitions for shuffles and RDDs; raising it does not enlarge executor heap and can worsen memory pressure. It is tempting because parallelism tuning often improves Spark performance, and it would be correct if the job were slow from too few partitions rather than failing with heap exhaustion.
- ✗
Increase spark.sql.shuffle.partitions
Why it's wrong here
spark.sql.shuffle.partitions controls partition count for shuffles, not heap allocation; too few partitions can cause skewed, oversized tasks, but the stem reports a heap error, not skew. It is tempting because more partitions reduce per-task data, yet the direct fix is raising executor memory.
- ✓
Increase spark.executor.memory
Why this is correct
Raising spark.executor.memory enlarges each executor's JVM heap, directly addressing the 'OutOfMemoryError: Java heap space' thrown during Spark execution. Since the failure occurs in executors rather than the driver, this parameter targets the constrained component, giving shuffle and aggregation buffers sufficient headroom to complete the S3-to-HDFS job.
- ✗
Increase spark.driver.memory
Why it's wrong here
Executor heap is governed by spark.executor.memory, not spark.driver.memory; the driver only coordinates the job and holds the SparkContext. Raising driver memory is tempting when the driver itself OOMs during collect() or large broadcast handling, but here the failure is explicitly in executors.
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.