DEA-C01 Data Operations and Support Practice Question
A data engineer is troubleshooting a nightly ETL job that reads data from an RDS MySQL instance and writes to an S3 bucket in Parquet format. The job runs on an EMR cluster and uses PySpark. Recently, the job started failing with 'OutOfMemoryError' in the executor logs. The data volume has grown 30% in the last month. Which is the MOST efficient solution to resolve this issue without changing the code?
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 executor memory and memoryOverhead in the Spark configuration.
The OutOfMemoryError in executors indicates insufficient memory per executor to handle the increased data volume. Increasing 'spark.executor.memory' and 'spark.executor.memoryOverhead' directly addresses this by providing more heap and off-heap memory without any code changes. Option A is wrong because the RDS instance size does not affect executor memory; the bottleneck is in Spark processing. Option B is wrong because switching to AWS Glue would require code changes and may still need memory tuning, making it less efficient. Option D is wrong because adding core nodes increases parallelism but does not increase memory per executor, so the OOM could still occur.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the RDS instance to a larger size to reduce load.
Why it's wrong here
The OutOfMemoryError occurs in Spark executors, not at the RDS source; increasing RDS size does not address the memory issue in Spark.
- ✗
Switch the ETL job to use AWS Glue with a larger WorkerType.
Why it's wrong here
Migrating to Glue requires code changes and may not resolve the memory issue if the root cause is insufficient Spark configuration.
- ✓
Increase the executor memory and memoryOverhead in the Spark configuration.
Why this is correct
Increasing executor memory and memoryOverhead directly addresses the OutOfMemoryError by providing more heap and off-heap memory to executors.
- ✗
Increase the number of core nodes in the EMR cluster.
Why it's wrong here
Adding more nodes increases parallelism but does not increase memory per executor, so the OutOfMemoryError may persist.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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