MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?
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
✓
Configure the tuning job with early stopping enabled.
SageMaker's automatic model tuning supports early stopping. When enabled, the tuning job stops automatically if no significant improvement is observed. Option B is incorrect because CloudWatch alarms can monitor metrics but cannot directly stop a tuning job; they can trigger actions like notifications but not stop the tuning. Option C is incorrect because SageMaker Experiments is for tracking experiments, not for stopping tuning jobs. Option D is incorrect because SageMaker Debugger stops individual training jobs, not the hyperparameter tuning job itself; early stopping is a built-in feature of the tuning job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the tuning job with early stopping enabled.
Why this is correct
Built-in early stopping stops underperforming training jobs.
- ✗
Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.
Why it's wrong here
CloudWatch alarms can stop instances but not SageMaker jobs directly.
- ✗
Use SageMaker Experiments to monitor and manually stop the tuning job.
Why it's wrong here
Manual intervention is not automated.
- ✗
Use SageMaker Debugger to stop training jobs that are not improving.
Why it's wrong here
Debugger stops individual training jobs, not the tuning job.
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