Question 1,034 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using SageMaker Processing jobs to run feature engineering on large datasets. The job takes a long time to complete. Which change would most likely reduce the processing time?
⚠ Common exam trap
Many exam-takers confuse scaling up (larger instance type) with scaling out (more instances), assuming that a bigger instance always yields faster processing, but for distributed data processing, horizontal scaling is usually more effective for large datasets.
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 instances in the processing cluster
Increasing the number of instances in the processing cluster enables SageMaker Processing to distribute the workload across multiple nodes, leveraging parallel processing to reduce the overall execution time. SageMaker Processing uses a distributed computing model where each instance processes a subset of the data, so adding more instances directly increases parallelism and throughput for embarrassingly parallel tasks like feature engineering.
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 instances in the processing cluster
Why this is correct
More instances allow parallel processing, reducing overall time.
- ✗
Switch to local mode to avoid network overhead
Why it's wrong here
Local mode does not use distributed processing and is for small datasets.
- ✗
Change the processing script from Python to PySpark
Why it's wrong here
Changing framework may not reduce time if the script is not optimized for Spark.
- ✗
Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge
Why it's wrong here
Larger instance improves performance but distributed processing scales better.
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Last reviewed: Jul 4, 2026
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