hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer runs a SageMaker…
A machine learning engineer runs a SageMaker HyperparameterTuningJob with Bayesian optimization strategy. The job terminates earlier than the specified MaxNumberOfTrainingJobs. The engineer notices that the best objective metric value has not improved for several consecutive jobs. What is the most likely adjustment to make?
⚠ Common exam trap
AWS often tests the misconception that early termination is caused by insufficient training jobs or search strategy, when in fact it is the early stopping tolerance that directly controls the termination condition in Bayesian optimization.
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
✓
Adjust the early stopping tolerance (e.g., increase the number of consecutive jobs with no improvement allowed).
The Bayesian optimization strategy in SageMaker HyperparameterTuningJob uses early stopping to halt the tuning job when the objective metric has not improved for a specified number of consecutive training jobs. The engineer observed that the job terminated earlier than MaxNumberOfTrainingJobs because the default early stopping tolerance was reached. Increasing the early stopping tolerance (e.g., raising the number of consecutive jobs with no improvement allowed) gives the Bayesian optimizer more chances to explore and potentially find a better configuration before stopping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Adjust the early stopping tolerance (e.g., increase the number of consecutive jobs with no improvement allowed).
Why this is correct
Early stopping is likely too aggressive; increasing the tolerance allows more exploration before terminating.
- ✗
Switch to a grid search strategy to cover all hyperparameter combinations.
Why it's wrong here
Grid search is inefficient and ignores the existing Bayesian model; resource waste is likely.
- ✗
Increase the MaxNumberOfTrainingJobs parameter to allow more exploration.
Why it's wrong here
If the search has converged, more jobs may not yield improvement. The issue is early stopping triggering too soon.
- ✗
Decrease the number of hyperparameters being tuned.
Why it's wrong here
Reducing hyperparameters might simplify the search but does not solve the premature stopping.
Go deeper
Related to this question
About these practice questions
One of 835 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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.