This MLS-C01 practice question tests your understanding of modeling. 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 company is using Amazon SageMaker to tune hyperparameters for a gradient boosting model. The objective is to minimize root mean squared error (RMSE). The data scientist wants to explore the hyperparameter space efficiently. Which THREE hyperparameter tuning strategies should the data scientist consider? (Choose 3.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Bayesian optimization
Uses probabilistic model to guide search.
Random search
Samples randomly, can be efficient.
Grid search
Why wrong: Exhaustive search, inefficient for large spaces.
Manual search
Why wrong: Not automated, inefficient for large spaces.
Hyperband
Early stopping with adaptive resource allocation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Bayesian optimization
Bayesian optimization is correct because it builds a probabilistic model of the objective function (RMSE) and uses an acquisition function to select the next hyperparameter combination to evaluate. This approach is sample-efficient, making it ideal for expensive-to-evaluate models like gradient boosting, as it balances exploration and exploitation to find optimal hyperparameters with fewer trials.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Bayesian optimization
Why this is correct
Uses probabilistic model to guide search.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Random search
Why this is correct
Samples randomly, can be efficient.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Grid search
Why it's wrong here
Exhaustive search, inefficient for large spaces.
Manual search
Why it's wrong here
Not automated, inefficient for large spaces.
Hyperband
Why this is correct
Early stopping with adaptive resource allocation.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often assume grid search is the most thorough strategy, but in practice it is inefficient for high-dimensional spaces, while SageMaker explicitly supports Bayesian optimization, random search, and Hyperband as the three built-in tuning strategies.
Detailed technical explanation
Bayesian optimization in SageMaker uses a Gaussian process prior to model the objective function, with an expected improvement (EI) acquisition function to guide sampling. Random search, while simpler, samples hyperparameters uniformly from defined ranges and often outperforms grid search in high-dimensional spaces because it explores more distinct values per dimension. Hyperband extends random search by dynamically allocating resources (e.g., training iterations) to promising configurations and early-stopping poor performers, which is particularly effective for gradient boosting where training time scales with data size.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Bayesian optimization — Bayesian optimization is correct because it builds a probabilistic model of the objective function (RMSE) and uses an acquisition function to select the next hyperparameter combination to evaluate. This approach is sample-efficient, making it ideal for expensive-to-evaluate models like gradient boosting, as it balances exploration and exploitation to find optimal hyperparameters with fewer trials.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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