- A
Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.
This order follows the standard SageMaker Automatic Model Tuning workflow: first define the metric to optimize, then specify the hyperparameter ranges, then set limits on jobs, then launch the tuning job, and finally review results to pick the best model.
- B
Define the hyperparameter search space, then configure resource limits, then define the objective metric, then create the tuning job, then monitor and select the best model.
Why wrong: This order is incorrect because the objective metric must be defined before setting resource limits and creating the tuning job, as it determines how the tuning evaluates trials.
- C
Select the best model, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job.
Why wrong: This order is incorrect because selecting the best model occurs after the tuning job completes, not before any configuration. You cannot select a model before defining the search space and running trials.
- D
Create the tuning job, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then monitor and select the best model.
Why wrong: This order is incorrect because you cannot create a tuning job without first defining the objective metric, hyperparameter ranges, and resource limits. The job creation requires these as input parameters.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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.
Drag and drop the steps to perform hyperparameter tuning using SageMaker Automatic Model Tuning in the correct order.
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
Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.
Tuning involves defining search space, creating a tuning job, setting limits, executing, and selecting best model.
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.
- ✓
Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.
Why this is correct
This order follows the standard SageMaker Automatic Model Tuning workflow: first define the metric to optimize, then specify the hyperparameter ranges, then set limits on jobs, then launch the tuning job, and finally review results to pick the best model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Define the hyperparameter search space, then configure resource limits, then define the objective metric, then create the tuning job, then monitor and select the best model.
Why it's wrong here
This order is incorrect because the objective metric must be defined before setting resource limits and creating the tuning job, as it determines how the tuning evaluates trials.
- ✗
Select the best model, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job.
Why it's wrong here
This order is incorrect because selecting the best model occurs after the tuning job completes, not before any configuration. You cannot select a model before defining the search space and running trials.
- ✗
Create the tuning job, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then monitor and select the best model.
Why it's wrong here
This order is incorrect because you cannot create a tuning job without first defining the objective metric, hyperparameter ranges, and resource limits. The job creation requires these as input parameters.
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 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 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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model. — Tuning involves defining search space, creating a tuning job, setting limits, executing, and selecting best model.
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.
What is the key concept behind this question?
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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