MLS-C01 Modeling Practice Question
A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?
⚠ Common exam trap
The trap here is that candidates mistakenly think early stopping (Option A) applies to the tuning job itself rather than to individual training jobs, or they assume changing the metric (Option B) will indirectly improve MAE, when in fact the tuning job's objective must directly match the business metric.
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
✓
Increase the total number of training jobs to 50.
Increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set early stopping to avoid overfitting.
Why it's wrong here
Early stopping prevents overfitting but doesn't necessarily improve the best objective value.
- ✗
Change the objective metric to RMSE.
Why it's wrong here
Changing the metric changes the problem, not necessarily improving MAE.
- ✓
Increase the total number of training jobs to 50.
Why this is correct
More jobs allow broader exploration and may find a better configuration.
- ✗
Switch the tuning strategy from Bayesian to Random search.
Why it's wrong here
Random search is less efficient than Bayesian for hyperparameter tuning.
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