MLS-C01 Data Engineering Practice Question
A company has an AWS Glue ETL job that reads data from an Amazon RDS for MySQL table and writes to Amazon S3 in Parquet format. The job runs daily and processes 500 GB of data. Recently, the job has been failing with memory errors during the write phase. The data schema is wide (200 columns). Which change should a data engineer make to the Glue job to resolve the memory issue?
⚠ Common exam trap
It's easy for candidates to assume memory errors are solved by adding more resources (DPUs) or by changing the output format, when the actual fix is a write-tuning parameter that controls per-file record limits.
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
✓
Configure the write operation with 'groupSize' to limit records per file.
The memory error occurs because the wide schema (200 columns) and large data volume (500 GB) cause the Spark executors to run out of memory when writing Parquet files, as each executor attempts to buffer entire partitions. Configuring 'groupSize' limits the number of records written per file, reducing the per-executor memory footprint and preventing out-of-memory errors during the write phase.
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 DPUs for the Glue job.
Why it's wrong here
More DPUs add cost; may not resolve memory issue if the problem is per-worker memory.
- ✗
Change the output format from Parquet to CSV.
Why it's wrong here
CSV does not reduce memory; may increase I/O.
- ✗
Use the JDBC connection with fetchSize parameter.
Why it's wrong here
fetchSize affects reading, not writing.
- ✓
Configure the write operation with 'groupSize' to limit records per file.
Why this is correct
Limiting records per file reduces the memory needed for buffering during writes.
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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