- A
k-fold cross-validation with shuffling
Why wrong: Shuffling breaks temporal dependencies.
- B
Random train-test split with 80/20 ratio
Why wrong: Random split ignores temporal order and causes look-ahead bias.
- C
Stratified sampling based on sales volume
Why wrong: Stratified sampling does not preserve time order.
- D
Walk-forward validation (time-series split)
Walk-forward validation uses expanding or sliding windows that respect time order.
Question 294 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer needs to split 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 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?
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 (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.
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.
- ✗
k-fold cross-validation with shuffling
Why it's wrong here
Shuffling breaks temporal dependencies.
- ✗
Random train-test split with 80/20 ratio
Why it's wrong here
Random split ignores temporal order and causes look-ahead bias.
- ✗
Stratified sampling based on sales volume
Why it's wrong here
Stratified sampling does not preserve time order.
- ✓
Walk-forward validation (time-series split)
Why this is correct
Walk-forward validation uses expanding or sliding windows that respect time order.
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 k-fold cross-validation or random splits because they are standard for non-temporal data, failing to recognize that time-series data requires strict temporal ordering to avoid look-ahead bias.
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 forward in time, ensuring that each test set always follows the training set chronologically. In practice, for a 3-year daily sales dataset, you might train on the first 2 years and test on the next 6 months, then retrain on 2.5 years and test on the next 3 months, etc. This method is critical for financial or inventory forecasting where temporal dependencies are strong, and it directly mirrors how models are deployed in production—trained on historical data to predict unseen future points.
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 (time-series split) — 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.
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
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