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MLA-C01 Practice Question: A machine learning team is training a large…
A machine learning team is training a large natural language processing model on Amazon SageMaker using the SageMaker Hugging Face container. The training job runs on multiple instances and uses Managed Spot Training to reduce costs. However, the job frequently gets interrupted by Spot interruptions, causing long training times. What should the team do to mitigate this issue?
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
✓
Enable checkpointing and increase the number of save intervals
Enabling checkpointing and saving intermediate model states at appropriate intervals allows the training job to resume from the last checkpoint after a Spot interruption, significantly reducing wasted time. Increasing save intervals means more frequent saving, which reduces work lost. Reserved capacity does not help with interruptions; using larger instances doesn't prevent interruptions; disabling Spot increases cost.
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 reserved capacity with Savings Plans
Why it's wrong here
Reserved capacity is for On-Demand instances, not specific to Spot interruptions.
- ✗
Use a larger instance type to finish faster
Why it's wrong here
Larger instances may reduce training time but do not prevent interruptions; they may cost more on Spot.
- ✓
Enable checkpointing and increase the number of save intervals
Why this is correct
Checkpointing saves model state so training can resume after a Spot interruption; more frequent saves reduce the amount of work lost.
- ✗
Disable Managed Spot Training and use On-Demand instances
Why it's wrong here
This would increase costs and does not leverage the cost savings of Spot.
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