MLS-C01 Data Engineering Practice Question
A company uses AWS Glue ETL jobs to process data from multiple sources. The job fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Insufficient memory.' The job runs on a G.1X worker type with 10 workers. What should be changed to resolve this error?
⚠ Common exam trap
Test-takers frequently confuse 'insufficient memory' with a per-worker memory limit and choose to upgrade the worker type (G.2X), but the error is about total cluster memory, which is more effectively addressed by increasing the number of workers.
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 workers to 20.
The error 'Insufficient memory' in AWS Glue ETL jobs typically indicates that the total memory across all executors is insufficient for the data being processed. Increasing the number of workers from 10 to 20 doubles the total memory and compute capacity available, allowing the job to handle larger datasets without running out of memory. This is the most direct and effective fix for a memory exhaustion error when using the G.1X worker type.
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 the number of workers to 20.
Why this is correct
More workers increase parallelism and reduce memory pressure per worker.
- ✗
Enable the Spark UI to monitor the job.
Why it's wrong here
Monitoring does not fix memory issues.
- ✗
Change the worker type to G.2X.
Why it's wrong here
This may help but increasing number of workers is a more direct solution for parallelism.
- ✗
Reduce the number of partitions in the DynamicFrame.
Why it's wrong here
Reducing partitions may cause out-of-memory in fewer partitions.
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