The answer is to increase the exploration weight in the SageMaker tuning job configuration. This is correct because Bayesian optimization relies on an acquisition function that balances exploitation—focusing on known high-performing regions—against exploration, which samples more diverse hyperparameter combinations. By raising the exploration weight, you shift that balance, forcing the algorithm to investigate unfamiliar areas of the search space, which is precisely what you need when the validation accuracy has plateaued at 0.85. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how Bayesian methods handle the exploration-exploitation trade-off, a core concept in hyperparameter tuning. A common trap is confusing exploration weight with the number of concurrent training jobs or early stopping rules; neither directly increases search diversity. Memory tip: think of it as turning up the “wanderlust” dial—more exploration helps you escape local plateaus and discover better valleys.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is using Amazon SageMaker for hyperparameter tuning. The tuning job uses a Bayesian optimization strategy. After 10 training jobs, the objective metric (validation accuracy) has plateaued at 0.85. The data scientist wants to explore more diverse hyperparameter combinations. What should the data scientist do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the exploration weight in the tuning job configuration.
In Bayesian optimization, the exploration weight controls the trade-off between exploring new hyperparameter regions and exploiting known good regions. Increasing this weight encourages the acquisition function to sample more diverse combinations, which can help escape a plateau. Option C is correct because it directly addresses the need for greater diversity in the search space.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Decrease the exploration weight in the tuning job configuration.
Why it's wrong here
Decreasing exploration weight makes the search more exploitative, worsening the plateau.
✗
Switch to random search strategy.
Why it's wrong here
Random search might help but is less efficient; Bayesian with adjusted weights is better.
✓
Increase the exploration weight in the tuning job configuration.
Why this is correct
Increasing exploration weight prompts the algorithm to try more diverse combinations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of parallel training jobs.
Why it's wrong here
Parallelism does not alter the search strategy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing parallel jobs or switching to random search is the best way to increase diversity, when in fact Bayesian optimization's exploration weight is the precise control for this purpose.
Detailed technical explanation
How to think about this question
Bayesian optimization uses an acquisition function (e.g., Expected Improvement or Upper Confidence Bound) that balances exploration and exploitation via a tunable parameter (e.g., kappa in UCB). Increasing this parameter widens the confidence intervals, making the algorithm more likely to sample points with high uncertainty, thus exploring more diverse hyperparameter combinations. In practice, this is configured in SageMaker's HyperparameterTuningJobConfig via the 'TuningStrategy' and 'HyperparameterTuningJobObjective' settings, where exploration weight is adjusted through the 'ExplorationWeight' parameter in the 'BayesianOptimizationConfig'.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the exploration weight in the tuning job configuration. — In Bayesian optimization, the exploration weight controls the trade-off between exploring new hyperparameter regions and exploiting known good regions. Increasing this weight encourages the acquisition function to sample more diverse combinations, which can help escape a plateau. Option C is correct because it directly addresses the need for greater diversity in the search space.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Variation 1. A data scientist is performing hyperparameter tuning using Amazon SageMaker Automatic Model Tuning (AMT). The job uses a random search strategy. After 20 training jobs, the best objective metric value has plateaued. The data scientist wants to explore more of the hyperparameter space. Which action should the data scientist take?
medium
✓ A.Change the tuning strategy from Random to Bayesian.
B.Enable early stopping.
C.Decrease the maximum number of training jobs.
D.Increase the number of parallel training jobs.
Why A: Option C is correct because switching to Bayesian search will explore new regions based on previous results, potentially finding better hyperparameters. Option A is wrong because increasing the number of parallel jobs does not change the search strategy. Option B is wrong because decreasing the number of training jobs reduces exploration. Option D is wrong because early stopping does not change the search strategy.
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This MLS-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 MLS-C01 exam.
Question Discussion
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