The answer is to use random search strategy and Bayesian optimization as the two best practices for tuning hyperparameters with SageMaker Automatic Model Tuning. Random search is preferred over grid search because it explores the hyperparameter space more efficiently, especially when many hyperparameters are involved, as it does not waste trials on irrelevant dimensions. Bayesian optimization builds a probabilistic model of the objective function to intelligently select the next set of hyperparameters, balancing exploration and exploitation to converge on optimal values with fewer training jobs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of supported tuning strategies and cost-awareness—a common trap is assuming grid search is a best practice, but it scales poorly and is less efficient. Another trap is selecting a large number of training jobs, which drives up cost without proportional benefit. Remember the memory tip: "Random for range, Bayesian for brains"—random search handles wide spaces, while Bayesian optimization smartly narrows the search.
MLS-C01 Modeling Practice Question
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.
Which TWO actions are best practices for tuning hyperparameters using Amazon SageMaker Automatic Model Tuning?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Bayesian optimization strategy
Using random search or Bayesian optimization are supported strategies. Grid search is also possible but not efficient for many hyperparameters. Setting a large number of training jobs can be costly. Using the same hyperparameters as the baseline does not tune. So correct are A and C. B: Grid search is less efficient. D: Large number of jobs is not a best practice due to cost. E: Not tuning is not a best practice.
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.
✗
Set the number of training jobs to a very large value
Why it's wrong here
Large number of jobs increases cost unnecessarily.
✗
Use the same hyperparameters as the baseline model
Why it's wrong here
That does not constitute tuning.
✓
Use Bayesian optimization strategy
Why this is correct
Bayesian optimization is effective and efficient.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use grid search strategy
Why it's wrong here
Grid search is computationally expensive.
✓
Use random search strategy
Why this is correct
Random search is efficient for hyperparameter tuning.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Bayesian optimization strategy — Using random search or Bayesian optimization are supported strategies. Grid search is also possible but not efficient for many hyperparameters. Setting a large number of training jobs can be costly. Using the same hyperparameters as the baseline does not tune. So correct are A and C. B: Grid search is less efficient. D: Large number of jobs is not a best practice due to cost. E: Not tuning is not a best practice.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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