Question 589 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?
⚠ Common exam trap
A common mix-up: candidates confuse a memory error with a storage or format issue, leading them to choose Parquet (Option C) or Spark (Option A), when the actual fix is to allocate more RAM to the processing instance.
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 instance memory size for the processing job
The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a SageMaker Spark container instead of scikit-learn
Why it's wrong here
Switching to Spark may help, but increasing instance memory is a direct fix.
- ✓
Increase the instance memory size for the processing job
Why this is correct
More memory allows larger datasets to be processed in memory.
- ✗
Write the output as Parquet instead of CSV
Why it's wrong here
Changing output format does not reduce memory during processing.
- ✗
Standardize the features before loading into the DataFrame
Why it's wrong here
Standardization order does not affect memory usage.
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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Last reviewed: Jun 24, 2026
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