MLS-C01 Modeling Practice Question
A data scientist is training a model using SageMaker and wants to automatically stop training when the model stops improving. Which TWO options can be used?
⚠ Common exam trap
Many candidates confuse SageMaker Model Monitor (post-deployment monitoring) with SageMaker Debugger (training-time monitoring), or assume CloudWatch Alarms can directly implement early stopping logic when they are only for threshold-based alerts on emitted metrics.
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
✓
Built-in early stopping in XGBoost
Built-in early stopping in XGBoost (Option B) is correct because XGBoost natively supports an `early_stopping_rounds` parameter that halts training when the validation metric stops improving for a specified number of rounds. SageMaker Debugger (Option C) is correct because it can monitor training metrics in real time and trigger a stop action via a built-in or custom rule (e.g., `VanishingGradient` or `LossNotDecreasing`) when the model stops improving, integrating with SageMaker's `StopTraining` API.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Step Functions
Why it's wrong here
Orchestration, not automatic stopping.
- ✓
Built-in early stopping in XGBoost
Why this is correct
Native early stopping support.
- ✓
SageMaker Debugger
Why this is correct
Can monitor and stop training.
- ✗
CloudWatch Alarms
Why it's wrong here
For infrastructure monitoring.
- ✗
SageMaker Model Monitor
Why it's wrong here
For production data quality.
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