- A
Random shuffle followed by a 80/20 split
Why wrong: Random shuffle breaks temporal order and causes leakage.
- B
K-fold cross-validation with shuffled folds
Why wrong: Shuffled folds leak future information into training.
- C
Stratified sampling based on the target variable
Why wrong: Stratified sampling does not preserve temporal order.
- D
Time-series split (walk-forward validation)
Respects temporal order: trains on past, tests on future.
Question 49 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist needs to split a time-series…
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 split a time-series dataset into training and testing sets while avoiding data leakage from future values. Which splitting technique should the data scientist use?
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
✓
Time-series split (walk-forward validation)
Time-series split (walk-forward validation) ensures that the training set only contains past data relative to the test set, preventing leakage. Stratified sampling and random shuffle are not appropriate for time series because they ignore temporal order. K-fold cross-validation without adjustments will also leak future information.
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.
- ✗
Random shuffle followed by a 80/20 split
Why it's wrong here
Random shuffle breaks temporal order and causes leakage.
- ✗
K-fold cross-validation with shuffled folds
Why it's wrong here
Shuffled folds leak future information into training.
- ✗
Stratified sampling based on the target variable
Why it's wrong here
Stratified sampling does not preserve temporal order.
- ✓
Time-series split (walk-forward validation)
Why this is correct
Respects temporal order: trains on past, tests on future.
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 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 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
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: Time-series split (walk-forward validation) — Time-series split (walk-forward validation) ensures that the training set only contains past data relative to the test set, preventing leakage. Stratified sampling and random shuffle are not appropriate for time series because they ignore temporal order. K-fold cross-validation without adjustments will also leak future information.
What should I do if I get this MLA-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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