- A
Leave-one-out cross-validation
Why wrong: Leave-one-out is not appropriate for time series and would leak temporal information.
- B
Random 80/20 train-test split
Why wrong: Random split ignores temporal dependencies and may use future data to predict past, causing data leakage.
- C
Stratified k-fold cross-validation
Why wrong: Stratified splitting is for preserving class distribution, not temporal order; it also leaks future information.
- D
Walk-forward validation (time-series split)
Walk-forward validation sequentially trains on past and validates on future, mimicking the forecasting scenario.
Question 179 of 1,000
hardMultiple ChoiceObjective-mapped
Walk-Forward Validation for Time Series Forecasting
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.
An ML engineer is preparing a time-series dataset for a forecasting model that predicts daily sales for the next 30 days. The dataset contains 3 years of daily sales data. Which data splitting strategy should the engineer use to evaluate the model's performance on future data?
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 (time-series split)
Walk-forward validation (also called time-series cross-validation) respects temporal order and uses expanding or sliding windows to simulate sequential forecasting. Random holdout or k-fold would leak future information into training.
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.
- ✗
Leave-one-out cross-validation
Why it's wrong here
Leave-one-out is not appropriate for time series and would leak temporal information.
- ✗
Random 80/20 train-test split
Why it's wrong here
Random split ignores temporal dependencies and may use future data to predict past, causing data leakage.
- ✗
Stratified k-fold cross-validation
Why it's wrong here
Stratified splitting is for preserving class distribution, not temporal order; it also leaks future information.
- ✓
Walk-forward validation (time-series split)
Why this is correct
Walk-forward validation sequentially trains on past and validates on future, mimicking the forecasting scenario.
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: Walk-forward validation (time-series split) — Walk-forward validation (also called time-series cross-validation) respects temporal order and uses expanding or sliding windows to simulate sequential forecasting. Random holdout or k-fold would leak future information into training.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer needs to split a time-series dataset for a forecasting model. The data spans 3 years of daily sales. Which splitting strategy should they use to avoid look-ahead bias?
medium- A.k-fold cross-validation with shuffling
- B.Random train-test split with 80/20 ratio
- C.Stratified sampling based on sales volume
- ✓ D.Walk-forward validation (time-series split)
Why D: Walk-forward validation (time-series split) is the correct strategy because it preserves the temporal order of the data, training on past observations and testing on future observations sequentially. This avoids look-ahead bias, where future information leaks into the training set, which would invalidate the forecasting model's performance metrics.
Variation 2. A team needs to split a time-series dataset for a forecasting model. They want to avoid data leakage and evaluate model performance on future unseen data. Which data splitting strategy should they use?
medium- A.Holdout validation with random sampling
- ✓ B.Walk-forward validation
- C.K-fold cross-validation
- D.Random stratified split
Why B: Walk-forward validation is the correct strategy for time-series forecasting because it preserves the temporal order of data, training on past observations and testing on future ones in sequential steps. This avoids data leakage by ensuring that no future information is used to predict past events, which is critical for evaluating model performance on unseen future data.
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Last reviewed: Jul 4, 2026
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