The answer is to switch the hyperparameter tuning strategy to 'Bayesian'. This is the correct approach because Bayesian optimization builds a probabilistic model of the objective function using past training job results, allowing it to intelligently select the next hyperparameter combination that is most likely to improve the metric, making it far more sample-efficient than random search when the best metric has plateaued. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how SageMaker’s built-in tuning strategies differ in convergence speed and cost efficiency; a common trap is assuming that simply increasing the number of jobs will help, but that violates the constraint of not increasing total jobs, while changing the objective metric or scaling features does not address the search strategy itself. Remember the key distinction: random search explores blindly, while Bayesian search exploits past knowledge—think of it as “Bayesian builds on history, random rolls the dice.”
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.
A data scientist is using Amazon SageMaker to perform hyperparameter tuning for a neural network. The tuning job uses the 'Random' search strategy. After 10 training jobs, the best objective metric has plateaued. The scientist wants to improve the results without increasing the total number of training jobs. Which approach should they take?
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
✓
Switch the hyperparameter tuning strategy to 'Bayesian'
Switching to Bayesian search (e.g., 'Bayesian' strategy) is more efficient because it uses past results to choose the next hyperparameters, potentially finding better values in fewer jobs. Increasing the number of jobs would increase cost. Random search might get lucky but is less efficient. Changing the objective metric or scaling features would not directly improve the tuning process.
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.
✗
Use a different objective metric that is easier to optimize
Why it's wrong here
The metric should reflect the business problem; changing it arbitrarily is not appropriate.
✗
Normalize the input features to have zero mean and unit variance
Why it's wrong here
Feature scaling is important for training but does not directly affect hyperparameter tuning efficiency.
✗
Increase the maximum number of training jobs
Why it's wrong here
This would increase cost and time, not necessarily improve results per job.
✓
Switch the hyperparameter tuning strategy to 'Bayesian'
Why this is correct
Bayesian optimization uses past trials to inform future hyperparameter choices, often converging faster.
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: Switch the hyperparameter tuning strategy to 'Bayesian' — Switching to Bayesian search (e.g., 'Bayesian' strategy) is more efficient because it uses past results to choose the next hyperparameters, potentially finding better values in fewer jobs. Increasing the number of jobs would increase cost. Random search might get lucky but is less efficient. Changing the objective metric or scaling features would not directly improve the tuning process.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.