- A
Increase the number of DPUs for the Glue job.
Why wrong: More DPUs add cost; may not resolve memory issue if the problem is per-worker memory.
- B
Change the output format from Parquet to CSV.
Why wrong: CSV does not reduce memory; may increase I/O.
- C
Use the JDBC connection with fetchSize parameter.
Why wrong: fetchSize affects reading, not writing.
- D
Configure the write operation with 'groupSize' to limit records per file.
Limiting records per file reduces the memory needed for buffering during writes.
Question 1,565 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
When writing Parquet, Spark uses a columnar in-memory buffer for each partition; with 200 columns, each row's metadata and dictionary encoding consume significant memory. The 'groupSize' parameter (default 1024 records) controls the row group size within Parquet files—reducing it lowers the per-executor buffer allocation, allowing more partitions to be written without spilling to disk or OOM. In real-world scenarios, tuning 'groupSize' to 512 or 256 can resolve memory errors without sacrificing compression efficiency.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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