- A
Holdout validation with random sampling
Why wrong: Random holdout also mixes past and future.
- B
Walk-forward validation
Walk-forward validation trains on past data and tests on future data in sequential order.
- C
K-fold cross-validation
Why wrong: K-fold cross-validation shuffles data, not suitable for time series.
- D
Random stratified split
Why wrong: Random split shuffles data, causing temporal leakage because future data appears in training.
Question 847 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team needs to split a time-series dataset for a…
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 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?
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
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.
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.
- ✗
Holdout validation with random sampling
Why it's wrong here
Random holdout also mixes past and future.
- ✓
Walk-forward validation
Why this is correct
Walk-forward validation trains on past data and tests on future data in sequential order.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
K-fold cross-validation
Why it's wrong here
K-fold cross-validation shuffles data, not suitable for time series.
- ✗
Random stratified split
Why it's wrong here
Random split shuffles data, causing temporal leakage because future data appears in training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that standard cross-validation techniques like k-fold or random holdout are universally applicable, but the trap here is that they fail for time-series data because they ignore temporal dependencies, leading to data leakage and invalid performance metrics.
Detailed technical explanation
How to think about this question
Walk-forward validation, also known as time-series cross-validation, works by expanding or sliding a training window over time, where each fold uses a contiguous block of past data for training and the next time step for testing. This mimics real-world deployment where models are retrained on new data and evaluated on the immediate future. A subtle behavior is that the number of folds must be chosen carefully to balance between having enough training data and sufficient test points for reliable error estimation.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — 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.
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.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.