Question 137 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?
⚠ Common exam trap
AWS often tests the misconception that increasing instance size or using swap space is the primary solution for memory issues, whereas the correct approach is to distribute the workload horizontally using SageMaker's built-in data sharding feature.
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 Processing job with multiple instances and use ShardedByS3Key for data splitting.
SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.
Why this is correct
This distributes the data across instances, leveraging parallel processing and reducing memory per instance.
- ✗
Write the script to process data in chunks and write intermediate results to local ephemeral storage.
Why it's wrong here
Chunking helps but a single instance still has aggregated memory limits; multi-instance is better.
- ✗
Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.
Why it's wrong here
A larger instance is costly and may still not suffice; horizontal scaling is more effective.
- ✗
Reduce the number of instances to one and increase the volume size for swap space.
Why it's wrong here
Swap space is slower and does not solve memory limit for in-memory processing.
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 30, 2026
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