- A
Use the same SageMaker instance type
Why wrong: Instance type does not affect data splitting.
- B
Use the same hyperparameter values
Why wrong: Hyperparameters do not control data splitting.
- C
Use the same dataset version
Why wrong: Dataset version ensures same data but not same split.
- D
Set a random seed in the training script
Correct: Setting a random seed ensures reproducibility of random operations like data splits.
Question 53 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist needs to ensure that the same…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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?
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
✓
Set a random seed in the training script
Setting a random seed in the training script ensures that the pseudo-random number generator used for splitting the dataset produces the same sequence of random indices across runs. This guarantees an identical train/test split regardless of instance type, hyperparameters, or dataset version, which is essential for reproducibility in SageMaker experiments.
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 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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse environmental consistency (instance type, dataset version) with algorithmic determinism, overlooking that reproducibility of data splits requires explicit control of the random seed in code.
Detailed technical explanation
How to think about this question
Under the hood, Python's random module and libraries like scikit-learn (e.g., `train_test_split`) rely on a global random state or a `RandomState` object. By explicitly setting `random_state` or `np.random.seed`, you control the pseudo-random number generator's initial state, making the split deterministic. In SageMaker, this is critical when running multiple training jobs with the same data, as even slight variations in split can lead to different validation metrics and model comparisons.
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.
TExam Day Tips
- 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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Set a random seed in the training script — Setting a random seed in the training script ensures that the pseudo-random number generator used for splitting the dataset produces the same sequence of random indices across runs. This guarantees an identical train/test split regardless of instance type, hyperparameters, or dataset version, which is essential for reproducibility in SageMaker experiments.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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