- A
Reduce model complexity (e.g., fewer features, simpler model).
Simpler models have lower variance.
- B
Use regularization.
Regularization (e.g., L1/L2) penalizes large coefficients, reducing variance.
- C
Add more training data.
More data helps the model generalize, reducing variance.
- D
Remove outliers from the training data.
Why wrong: Removing outliers can sometimes reduce variance but may also introduce bias; it is not a primary variance reduction technique.
- E
Increase model complexity.
Why wrong: Increasing complexity (e.g., more features, deeper network) typically increases variance.
Question 347 of 1,000
Reducing High Variance in Machine Learning
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 company is deploying a machine learning model that predicts customer churn. The model currently has high variance. Which THREE actions should the data scientist take to reduce variance? (Select THREE.)
Quick Answer
The answer is to add more training data, reduce model complexity, and apply regularization. These three actions directly address high variance by forcing the model to generalize rather than memorize noise. Adding more training data exposes the model to a broader range of patterns, while reducing complexity—such as using fewer features or a simpler algorithm—limits its ability to overfit. Regularization penalizes overly large coefficients, further constraining the model’s flexibility. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the bias-variance tradeoff, a core concept in model evaluation. A common trap is confusing variance reduction with increasing model complexity or relying on outlier removal, which is not a standard primary technique. Remember the mnemonic “MORE Data, LESS Complexity, REGularize” to recall the three pillars of reducing high variance in machine learning.
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
✓
Reduce model complexity (e.g., fewer features, simpler model).
Option A is correct because reducing model complexity (e.g., using fewer features or a simpler algorithm like logistic regression instead of a deep neural network) directly addresses high variance by limiting the model's capacity to overfit to noise in the training data. A simpler model has less flexibility to capture spurious patterns, which reduces the gap between training and validation error.
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.
- ✓
Reduce model complexity (e.g., fewer features, simpler model).
Why this is correct
Simpler models have lower variance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use regularization.
Why this is correct
Regularization (e.g., L1/L2) penalizes large coefficients, reducing variance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add more training data.
Why this is correct
More data helps the model generalize, reducing variance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove outliers from the training data.
Why it's wrong here
Removing outliers can sometimes reduce variance but may also introduce bias; it is not a primary variance reduction technique.
- ✗
Increase model complexity.
Why it's wrong here
Increasing complexity (e.g., more features, deeper network) typically increases variance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that removing outliers is a universal fix for variance, when in reality it can harm generalization, and that increasing complexity is a solution for underfitting, not overfitting.
Detailed technical explanation
How to think about this question
High variance occurs when a model learns the training data too well, including its noise, leading to poor generalization. Regularization (Option B) works by adding a penalty term (e.g., L1 or L2) to the loss function, which shrinks coefficient magnitudes and reduces overfitting. Adding more training data (Option C) helps because it provides a more representative sample of the underlying distribution, smoothing out spurious correlations and reducing the model's sensitivity to individual data 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 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?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce model complexity (e.g., fewer features, simpler model). — Option A is correct because reducing model complexity (e.g., using fewer features or a simpler algorithm like logistic regression instead of a deep neural network) directly addresses high variance by limiting the model's capacity to overfit to noise in the training data. A simpler model has less flexibility to capture spurious patterns, which reduces the gap between training and validation error.
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.
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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