MLS-C01 Modeling Practice Question
Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?
⚠ Common exam trap
It's easy for candidates to confuse 'avoiding interruptions' with disabling automatic scaling, when in fact automatic scaling is designed to prevent interruptions by dynamically adjusting capacity, and disabling it increases the risk of failures.
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 Debugger to profile system bottlenecks
SageMaker Debugger is a best practice because it provides real-time profiling of system bottlenecks such as CPU/GPU utilization, memory I/O, and network throughput during training. This allows you to identify and resolve performance issues early, optimizing training efficiency and cost. It integrates directly with SageMaker's training jobs without requiring code changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable automatic scaling to avoid interruptions
Why it's wrong here
Automatic scaling helps manage resources efficiently; disabling it may cause performance issues.
- ✓
Use SageMaker Debugger to profile system bottlenecks
Why this is correct
Debugger provides insights into GPU utilization and I/O bottlenecks.
- ✓
Use Pipe mode for training data stored in S3 to reduce startup time
Why this is correct
Pipe mode streams data directly from S3, reducing download time and disk space.
- ✗
Always use the largest instance type available for faster training
Why it's wrong here
Larger instances may not always be cost-effective or necessary; choose based on workload.
- ✓
Use managed spot training to reduce cost
Why this is correct
Managed spot training can significantly reduce costs by using spare EC2 capacity.
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