MLA-C01 Data Preparation for Machine Learning Practice Question
A company uses SageMaker Processing jobs to clean customer transaction data. The processing script runs on a single ml.m5.large instance and takes 30 minutes to process 50 GB of data in CSV format. To reduce processing time, the company wants to process 200 GB of data within 1 hour. Which combination of changes should the company make?
⚠ Common exam trap
Test-takers frequently assume vertical scaling (larger instance) is sufficient, but the MLA-C01 exam tests understanding that horizontal scaling combined with data format optimization (Parquet) is required to meet strict time constraints 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 InstanceCount to 4 and convert the data to Parquet format.
Increasing InstanceCount to 4 allows parallel processing of the 200 GB dataset across multiple ml.m5.large instances, each handling 50 GB, which directly reduces processing time. Converting the data from CSV to Parquet format further accelerates processing by enabling columnar storage and predicate pushdown, reducing I/O and CPU overhead. Together, these changes can achieve the goal of processing 200 GB within 1 hour, as the original 50 GB took 30 minutes on a single instance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the job in local mode with a larger EBS volume.
Why it's wrong here
Local mode is for testing on the notebook instance and does not scale.
- ✗
Increase VolumeSizeInGB to 100 and use gzip compression.
Why it's wrong here
Volume size is not a performance bottleneck; compression may reduce I/O but not parallelism.
- ✓
Increase InstanceCount to 4 and convert the data to Parquet format.
Why this is correct
Multiple instances provide parallelism, and Parquet reduces I/O.
- ✗
Use a larger instance type (e.g., ml.r5.4xlarge) and keep the same script.
Why it's wrong here
Increasing instance type helps but scaling is not linear; improved parallelism from multiple instances is needed.
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