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MLA-C01 Practice Question: A team is training a deep learning model on…
A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)
⚠ Common exam trap
AWS often tests the misconception that bigger instances always mean faster training, but the real optimization lies in data pipeline efficiency (e.g., Pipe mode, compression, and shuffling) and cost management (e.g., Managed Spot Training with checkpointing).
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
✓
Store training data in Amazon S3 in a shuffled and compressed format
Storing training data in Amazon S3 in a shuffled and compressed format (Option A) optimizes training performance because shuffling prevents biased gradient updates during stochastic gradient descent, while compression reduces I/O overhead and network transfer time. SageMaker's Pipe mode can then stream this compressed data directly to the training algorithm without intermediate disk writes, further accelerating throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store training data in Amazon S3 in a shuffled and compressed format
Why this is correct
Shuffling prevents bias and compression reduces transfer time, improving training performance.
- ✗
Use the largest instance type available
Why it's wrong here
Largest instance types increase cost and may not yield proportional performance gains; optimization should consider cost-efficiency.
- ✗
Increase the number of layers in the model to improve accuracy
Why it's wrong here
Deeper models may improve accuracy but also increase training time and risk overfitting; this is not a general performance optimization practice.
- ✓
Use SageMaker Managed Spot Training with checkpointing
Why this is correct
Spot instances are cheaper, and checkpointing allows resuming after interruptions, providing both cost savings and reliability.
- ✓
Use Pipe mode to stream data instead of File mode
Why this is correct
Pipe mode streams data directly from S3 to the training container, reducing disk usage and I/O wait.
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
Senior Network & Security Engineer · founder of Courseiva
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