- A
It reduces training time by using all data for training simultaneously
Why wrong: Cross-validation actually increases training time because the model is trained multiple times.
- B
It eliminates the need for feature engineering
Why wrong: Cross-validation does not replace feature engineering; it is an evaluation technique.
- C
It provides a more reliable estimate of model performance on unseen data
Cross-validation averages performance across folds, giving a more stable and less biased estimate.
- D
It guarantees that the model will achieve high accuracy on the test set
Why wrong: Cross-validation does not guarantee high accuracy; it only helps assess performance.
Question 351 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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.
Which of the following is a benefit of using cross-validation during model training?
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
✓
It provides a more reliable estimate of model performance on unseen data
Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the 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.
- ✗
It reduces training time by using all data for training simultaneously
Why it's wrong here
Cross-validation actually increases training time because the model is trained multiple times.
- ✗
It eliminates the need for feature engineering
Why it's wrong here
Cross-validation does not replace feature engineering; it is an evaluation technique.
- ✓
It provides a more reliable estimate of model performance on unseen data
Why this is correct
Cross-validation averages performance across folds, giving a more stable and less biased estimate.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
It guarantees that the model will achieve high accuracy on the test set
Why it's wrong here
Cross-validation does not guarantee high accuracy; it only helps assess performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse cross-validation with a method to improve model accuracy or reduce training time, when in fact its primary purpose is to provide a more reliable estimate of model performance on unseen data.
Detailed technical explanation
How to think about this question
In k-fold cross-validation, the dataset is split into k equal-sized folds; the model is trained on k-1 folds and validated on the remaining fold, repeating this process k times. The final performance metric is averaged across all folds, which helps detect overfitting and provides a more stable estimate of how the model will perform on new, unseen data. In real-world scenarios, such as medical diagnosis or fraud detection, cross-validation is critical because a single train-test split might accidentally produce an overly optimistic or pessimistic evaluation due to data imbalance or random sampling.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: It provides a more reliable estimate of model performance on unseen data — Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the data.
What should I do if I get this AIF-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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