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?
⚠ Common exam trap
The trap is thinking more jobs or parallelism will always improve results; candidates may not realize that refining hyperparameter ranges is a more effective strategy when progress stalls.
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
When RMSE has plateaued, narrowing the hyperparameter ranges to focus on promising areas can help the tuner explore more finely around good values. This is a common technique in Bayesian optimization to refine the search. Increasing jobs or parallelism may not help if the search space is too broad.
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
Adding more jobs repeats the same search over an already-explored hyperparameter space, so RMSE stays plateaued. It is tempting because extra trials help when the tuner stopped early with budget remaining, and would be correct if the plateau resulted from too few completed jobs rather than exhausted search space.
- ✗
Increase the number of parallel training jobs
Why it's wrong here
Parallel jobs only speed up wall-clock tuning; they do not widen the explored hyperparameter ranges, so the plateau persists. It is tempting because parallelism reduces total tuning time, and would be correct when the constraint is job duration rather than search coverage or convergence quality.
- ✓
Decrease the range of hyperparameters to focus on promising areas
Why this is correct
Narrowing each hyperparameter's search range concentrates the tuner's sampling around regions that previously produced low RMSE, increasing the chance of finding better values. This satisfies the stem's plateaued-after-20-jobs constraint, where broad ranges waste trials on unpromising areas.
- ✗
Switch the objective metric to mean absolute error (MAE)
Why it's wrong here
Changing the objective metric abandons the stated RMSE goal and optimises a different quantity, so the reported RMSE need not improve. It is tempting because MAE is a valid regression metric, and would be correct if the business requirement were robustness to outliers rather than minimising RMSE.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.