MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using AWS Glue to prepare data for training. They notice that the ETL job takes a long time to process large datasets. Which change is most likely to improve performance?
⚠ Common exam trap
Candidates often think reducing workers or disabling shuffle will speed up the job, but they fail to recognize that AWS Glue's performance is primarily limited by parallelism, and reducing resources or core Spark operations will degrade or break the job.
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 for the Glue job.
Increasing the number of DPUs (Data Processing Units) for the AWS Glue job allocates more distributed computing resources, which allows the job to process data in parallel across more executors. This directly reduces the runtime for large datasets by improving the parallelism of Spark transformations and actions.
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 this is correct
More DPUs increase parallelism and speed up processing.
- ✗
Decrease the number of workers in the Glue job.
Why it's wrong here
Fewer workers reduce parallelism and increase time.
- ✗
Disable Spark shuffle operations.
Why it's wrong here
Shuffles are often necessary for joins/aggregations; disabling may break logic.
- ✗
Reduce the dataset size by sampling.
Why it's wrong here
Sampling loses data; not a performance improvement for full dataset.
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