Quick Answer
The answer is to set a random seed in the training script. This is correct because the random seed initializes the pseudo-random number generator used by libraries like scikit-learn or pandas to shuffle and split data; fixing the seed guarantees that the same indices are selected for training and testing every time the script runs, regardless of the instance type or environment. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that reproducibility in SageMaker depends on controlling randomness at the code level, not infrastructure—common traps include assuming that using the same dataset version or instance type alone ensures identical splits. A useful memory tip: think of the seed as a “split lock”—once set, every run produces the same train/test partition, making your experiments truly reproducible.
This MLA-C01 practice question tests your understanding of ml model development. 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 needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?
Use the same SageMaker instance type
Why wrong: Instance type does not affect data splitting.
Use the same hyperparameter values
Why wrong: Hyperparameters do not control data splitting.
Use the same dataset version
Why wrong: Dataset version ensures same data but not same split.
Set a random seed in the training script
Correct: Setting a random seed ensures reproducibility of random operations like data splits.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Set a random seed in the training script
Option C is correct because setting a random seed in the training script ensures reproducibility of the data split. Options A, B, and D are incorrect because instance type, dataset version, and hyperparameters do not control the random split.
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.
Use the same SageMaker instance type
Why it's wrong here
Instance type does not affect data splitting.
Use the same hyperparameter values
Why it's wrong here
Hyperparameters do not control data splitting.
Use the same dataset version
Why it's wrong here
Dataset version ensures same data but not same split.
Set a random seed in the training script
Why this is correct
Correct: Setting a random seed ensures reproducibility of random operations like data splits.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which MLA-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
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Set a random seed in the training script — Option C is correct because setting a random seed in the training script ensures reproducibility of the data split. Options A, B, and D are incorrect because instance type, dataset version, and hyperparameters do not control the random split.
Identify which MLA-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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
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