- A
Increase the number of folds
More folds mean each training set is larger and more similar to the full dataset, reducing variance.
- B
Use stratified cross-validation
Stratification preserves class proportions across folds, reducing variability due to sampling.
- C
Decrease the number of folds
Why wrong: Fewer folds result in smaller training sets and higher variance in estimates.
- D
Shuffle data before splitting
Why wrong: Shuffling is standard and does not directly address variance; it ensures randomness but may not reduce variance.
- E
Use a more complex model
Why wrong: More complex models increase variance, making the problem worse.
Question 532 of 1,000
Reduce Cross-Validation Variance — CompTIA AI+ Techniques
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 is using k-fold cross-validation to evaluate a model. They observe high variance in performance scores across folds. Which TWO actions are most likely to reduce this variance? (Choose TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The answer is to increase the number of folds and use stratified cross-validation. Increasing the number of folds, such as moving from 5 to 10, reduces variance because each fold trains on a larger proportion of the data, making the model’s performance estimates more stable across runs. Stratified cross-validation further reduces variance by ensuring each fold maintains the same class distribution as the full dataset, preventing skewed scores from imbalanced splits. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of how to reduce cross-validation variance without overcomplicating the evaluation process. A common trap is assuming that decreasing folds or using a more complex model will help, but both actually increase variance. Remember the memory tip: “More folds, more stable; stratify to stabilize.”
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
✓
Increase the number of folds
Increasing the number of folds (e.g., from 5 to 10) means each fold contains more training data, which reduces the variance of the performance estimate because the model is trained on a larger portion of the dataset each time. Stratified cross-validation ensures that each fold maintains the same class distribution as the original dataset, which stabilizes performance scores when the dataset is imbalanced, thereby reducing variance across folds.
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.
- ✓
Increase the number of folds
Why this is correct
More folds mean each training set is larger and more similar to the full dataset, reducing variance.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use stratified cross-validation
Why this is correct
Stratification preserves class proportions across folds, reducing variability due to sampling.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the number of folds
Why it's wrong here
Fewer folds result in smaller training sets and higher variance in estimates.
- ✗
Shuffle data before splitting
Why it's wrong here
Shuffling is standard and does not directly address variance; it ensures randomness but may not reduce variance.
- ✗
Use a more complex model
Why it's wrong here
More complex models increase variance, making the problem worse.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that decreasing the number of folds reduces variance, when in fact the opposite is true—fewer folds increase variance because each training set is smaller and more dissimilar.
Detailed technical explanation
How to think about this question
In k-fold cross-validation, the bias-variance tradeoff of the estimate is influenced by the number of folds: more folds reduce variance but increase bias (since training sets are more similar), while fewer folds increase variance but reduce bias. Stratified cross-validation is particularly effective for classification tasks with imbalanced classes, as it ensures each fold is a miniature representation of the overall class proportions, preventing folds with skewed distributions that cause high variance in metrics like accuracy or F1-score.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of folds — Increasing the number of folds (e.g., from 5 to 10) means each fold contains more training data, which reduces the variance of the performance estimate because the model is trained on a larger portion of the dataset each time. Stratified cross-validation ensures that each fold maintains the same class distribution as the original dataset, which stabilizes performance scores when the dataset is imbalanced, thereby reducing variance across folds.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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