AIF-C01 Fundamentals of AI and ML Practice Question

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. Training jobs are frequently failing with 'OutOfMemoryError'. The training algorithm uses PyTorch. How should the data scientist solve this without reducing model accuracy?

⚠ Common exam trap

The AIF-C01 exam often tests the misconception that reducing model complexity or instance size is the only way to fix memory errors, when in fact data ingestion mode changes (like Pipe mode) can resolve the issue without sacrificing accuracy or performance.

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 data ingestion

SageMaker Pipe mode streams training data directly from S3 into the algorithm without first downloading it to the local disk, which drastically reduces memory consumption. This allows the model to handle large datasets that would otherwise cause an OutOfMemoryError when using the default File mode, all while preserving the original model architecture and accuracy.

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 data ingestion
Why this is correct
Pipe mode streams data directly, reducing memory footprint and preventing OutOfMemoryError.
✗
Reduce the number of layers in the model
Why it's wrong here
Reducing model layers would change the architecture and potentially reduce accuracy.
✗
Increase the batch size
Why it's wrong here
Increasing batch size increases memory usage, worsening the problem.
✗
Use a smaller instance type with less memory
Why it's wrong here
Smaller instance has less memory, likely causing more out-of-memory errors.

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

Go deeper

AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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