MLA-C01 ML Model Development Practice Question
A company is training a large NLP model on SageMaker and wants to reduce costs by using Spot Instances. Which TWO configurations should they implement to handle Spot interruptions gracefully?
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
✓
Set `use_spot_instances=True` and `max_wait` in the estimator
Checkpointing saves progress so training can resume from the last checkpoint. Managed Spot Training with `use_spot_instances=True` automates handling of interruptions. Using a single instance or increasing max runtime does not handle interruptions; `keep_alive_period` is for persistent notebooks, not training.
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 a single large instance to reduce interruption probability
Why it's wrong here
Spot interruptions are independent of instance size; a single instance is still at risk.
- ✓
Set `use_spot_instances=True` and `max_wait` in the estimator
Why this is correct
Managed Spot Training automatically handles interruptions and relaunches jobs.
- ✗
Increase the `max_run` parameter to allow longer training
Why it's wrong here
`max_run` sets the total training time, but does not handle interruptions.
- ✗
Use `keep_alive_period` to keep the instance alive after training
Why it's wrong here
`keep_alive_period` is for SageMaker Studio apps, not training jobs.
- ✓
Enable checkpointing to save model state periodically
Why this is correct
Checkpointing allows resuming from the last saved state if a Spot instance is interrupted.
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