MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for a certain number of epochs. Which SageMaker feature can be used for this purpose?
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
✓
Custom early stopping callback in the training script
SageMaker provides built-in early stopping via the 'StoppingCondition' parameter in the training job definition, or through custom training scripts that use callbacks. The simplest way is to set MaxRuntimeInSeconds, but for early stopping based on loss, the data scientist should implement a custom callback in the training script.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments tracks trials but does not stop training jobs automatically.
- ✓
Custom early stopping callback in the training script
Why this is correct
Implementing a custom callback that stops training when loss stagnates is the most direct method.
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SageMaker Automatic Model Tuning
Why it's wrong here
Tuning runs multiple jobs but does not stop them based on loss plateau.
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SageMaker Debugger
Why it's wrong here
Debugger monitors training but does not stop jobs; it can trigger CloudWatch alarms.
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