- A
Random k-fold cross-validation
Why wrong: Random splits would mix future data into training, causing leakage.
- B
Single hold-out set with random selection
Why wrong: Random hold-out may introduce leakage and does not evaluate temporal stability.
- C
Stratified sampling based on the target variable
Why wrong: Stratified sampling does not preserve time order.
- D
Walk-forward validation with an expanding window
Walk-forward validation trains on past data and tests on immediate future data, respecting temporal dependencies.
Question 895 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML team is preparing time-series data for a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?
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
✓
Walk-forward validation with an expanding window
Walk-forward validation with an expanding window is the most appropriate strategy because it respects the temporal order of the data, ensuring that each training window contains only past observations and each validation window contains only future observations. This prevents data leakage and provides a realistic evaluation of how the model will perform on unseen future time steps, which is critical for demand forecasting.
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 k-fold cross-validation
Why it's wrong here
Random splits would mix future data into training, causing leakage.
- ✗
Single hold-out set with random selection
Why it's wrong here
Random hold-out may introduce leakage and does not evaluate temporal stability.
- ✗
Stratified sampling based on the target variable
Why it's wrong here
Stratified sampling does not preserve time order.
- ✓
Walk-forward validation with an expanding window
Why this is correct
Walk-forward validation trains on past data and tests on immediate future data, respecting temporal dependencies.
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 default to random k-fold cross-validation (Option A) because it is a standard technique for i.i.d. data, forgetting that time-series data requires strict temporal ordering to avoid data leakage.
Detailed technical explanation
How to think about this question
Walk-forward validation with an expanding window works by iteratively increasing the training set to include all past data up to a cutoff point, then validating on the next contiguous block of time steps. This mimics a production scenario where the model is retrained on all available historical data before predicting the next period. A subtle behavior is that the expanding window can lead to computational inefficiency as the dataset grows, but it maximizes the amount of training data for each validation step, which is often beneficial for capturing long-term trends in demand forecasting.
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: Walk-forward validation with an expanding window — Walk-forward validation with an expanding window is the most appropriate strategy because it respects the temporal order of the data, ensuring that each training window contains only past observations and each validation window contains only future observations. This prevents data leakage and provides a realistic evaluation of how the model will perform on unseen future time steps, which is critical for demand forecasting.
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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