MLS-C01 Modeling Practice Question

A team is training a deep learning model on Amazon SageMaker. The training job is slow because the data is stored in S3 as many small files. Which approach is MOST effective to improve training throughput?

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 SageMaker Pipe mode for training input

Using SageMaker Pipe mode streams data directly from S3, reducing startup time. Shuffling files or increasing instance count does not address the small file overhead. Using EFS would introduce latency.

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 SageMaker Pipe mode for training input
Why this is correct
Pipe mode streams data directly, avoiding the need to download all files first, improving throughput.
✗
Increase the number of ml.c5.xlarge instances
Why it's wrong here
More instances may help but do not solve the small file issue; overhead per file remains.
✗
Shuffle the S3 objects to randomize order
Why it's wrong here
Shuffling does not reduce the overhead of many small files.
✗
Use Amazon EFS instead of S3
Why it's wrong here
EFS may have higher latency and cost for large-scale training.

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 20, 2026

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