DEA-C01 Data Operations and Support Practice Question
A company runs a nightly batch processing pipeline using AWS Glue ETL jobs. The pipeline reads data from an Amazon S3 bucket, transforms it, and writes results to an Amazon Redshift cluster. Recently, the data volume has increased significantly, and some Glue jobs are failing with the error 'java.lang.OutOfMemoryError: Java heap space'. The data engineer needs to modify the job configuration to prevent these failures without changing the code. The job currently uses 10 DPUs and processes data in a single Spark DataFrame. Which of the following is the MOST effective solution?
⚠ Common exam trap
Many candidates think reducing DPUs or using coalesce reduces memory usage, but in reality, both actions increase memory pressure on individual executors, making OOM errors more likely.
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 number of DPUs to 20 and enable job bookmarking for incremental processing.
Increasing DPUs from 10 to 20 provides more memory and compute resources, directly addressing the 'java.lang.OutOfMemoryError: Java heap space' caused by insufficient memory for the single DataFrame. Enabling job bookmarking allows incremental processing, which reduces the volume of data processed per run, further mitigating memory pressure without code changes.
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 DPUs to 5 and increase the Spark executor memory by setting 'spark.executor.memory' in job parameters.
Why it's wrong here
Reducing DPUs reduces total memory, and executor memory cannot exceed total DPU memory.
- ✓
Increase the number of DPUs to 20 and enable job bookmarking for incremental processing.
Why this is correct
More DPUs increase total available memory; job bookmarking reduces data volumes by processing only new data.
- ✗
Change the script to use DynamicFrame instead of DataFrame and disable the 'spark.sql.shuffle.partitions' configuration.
Why it's wrong here
DynamicFrame does not inherently reduce memory usage; disabling shuffle partitions may cause out-of-memory errors.
- ✗
Add a 'coalesce(1)' operation before writing to Redshift to reduce the number of output files.
Why it's wrong here
Coalescing to one partition forces all data into a single executor, worsening memory pressure.
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 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.