MLS-C01 Data Engineering Practice Question
A machine learning team is using Amazon SageMaker to train models on a large dataset stored in Amazon S3. The dataset is 5 TB in size and is partitioned by date. The team wants to minimize data transfer costs and reduce training time by caching frequently accessed data locally on the training instances. The training instances are EC2 instances with attached Amazon EBS volumes. The team is considering using SageMaker Pipe mode to stream data directly from S3, but they are concerned about network bandwidth. Which approach should the team use to optimize data loading for training?
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
✓
Use Amazon FSx for Lustre as a high-performance file system linked to the S3 bucket, and mount it on the training instances.
Amazon FSx for Lustre is natively integrated with Amazon SageMaker as a data source, providing a high-performance file system that can be linked directly to an S3 bucket. It automatically caches frequently accessed data from S3 on the file system, reducing data transfer costs and training time by avoiding repeated downloads. The caching capability addresses network bandwidth concerns effectively. Option B is incorrect: SageMaker File mode uses EBS volumes, not Amazon EFS, and is not designed as a shared, cached file system across training jobs. Option C is incorrect: copying the entire 5 TB dataset to EBS volumes before each training job is time-consuming, increases costs, and does not provide efficient caching across jobs. Option D is incorrect: SageMaker Pipe mode streams data directly from S3 without caching, so it does not reduce repeated data transfers and may still face bandwidth issues.
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 Amazon FSx for Lustre as a high-performance file system linked to the S3 bucket, and mount it on the training instances.
Why this is correct
Correct. Amazon FSx for Lustre provides a high-performance file system that integrates with S3 and caches data locally on the training instances, reducing data transfer costs and training time.
- ✗
Use SageMaker File mode with Amazon EFS, which allows multiple training instances to share the same file system and caches data from S3.
Why it's wrong here
Incorrect. SageMaker File mode uses EBS volumes, not Amazon EFS, and does not provide a shared, cached file system across multiple jobs as described.
- ✗
Increase the size of the EBS volumes attached to the training instances and copy the entire dataset to the volumes before training.
Why it's wrong here
Incorrect. Copying the entire 5 TB dataset to EBS volumes before training is inefficient, time-consuming, and costly, and does not cache data across training jobs.
- ✗
Use SageMaker Pipe mode to stream data from S3 directly to the training algorithm, which automatically caches data in memory.
Why it's wrong here
Incorrect. SageMaker Pipe mode streams data from S3 directly without caching, so it does not reduce data transfer for repeated access and may still encounter network bandwidth issues.
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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Written by Johnson Ajibi, MSc IT Security
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