MLA-C01 ML Model Development Practice Question
A company is using SageMaker Automatic Model Tuning to optimize a regression model. They want to minimize the root mean squared error (RMSE). The tuner has completed 20 jobs, and the RMSE has plateaued. Which action should the data scientist take to potentially improve the results?
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
✓
Decrease the range of hyperparameters to focus on promising areas
Reducing the search space can help the tuner focus on more promising regions. Increasing parallelism or max jobs may explore the same plateau, while switching to a different algorithm altogether might not be necessary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum number of training jobs
Why it's wrong here
More jobs may not improve results if the search space is too broad and the tuner has already explored well.
- ✗
Increase the number of parallel training jobs
Why it's wrong here
Increasing parallelism may explore more points but does not guarantee escaping a plateau; it also increases cost.
- ✓
Decrease the range of hyperparameters to focus on promising areas
Why this is correct
Narrowing the search space concentrates trials in regions that previously yielded lower RMSE, potentially finding better values.
- ✗
Switch the objective metric to mean absolute error (MAE)
Why it's wrong here
Changing the objective metric changes the goal, but the team wants to minimize RMSE, so this does not help.
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