- A
Keep both features to maximize information
Why wrong: Keeping both can inflate variance and make the model less interpretable.
- B
Apply principal component analysis to combine them
Why wrong: PCA creates new components that are harder to interpret in the business context.
- C
Increase the regularization strength
Why wrong: Regularization helps but does not directly address interpretability; removing one feature is simpler.
- D
Remove one of the correlated features
Reducing multicollinearity improves stability and interpretability while retaining predictive power.
Question 135 of 1,000
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. 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 data scientist is building a churn prediction model for a subscription service. The dataset includes highly correlated features: ‘number of support tickets’ and ‘average response time’. Which action is BEST to ensure model accuracy and interpretability?
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
✓
Remove one of the correlated features
Removing one of the highly correlated features (D) is the best action because multicollinearity between 'number of support tickets' and 'average response time' can inflate the variance of coefficient estimates, making the model unstable and harder to interpret. By dropping one feature, you reduce redundancy without significant information loss, preserving both accuracy and interpretability in a linear or tree-based model.
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.
- ✗
Keep both features to maximize information
Why it's wrong here
Keeping both can inflate variance and make the model less interpretable.
- ✗
Apply principal component analysis to combine them
Why it's wrong here
PCA creates new components that are harder to interpret in the business context.
- ✗
Increase the regularization strength
Why it's wrong here
Regularization helps but does not directly address interpretability; removing one feature is simpler.
- ✓
Remove one of the correlated features
Why this is correct
Reducing multicollinearity improves stability and interpretability while retaining predictive power.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that keeping all features maximizes information (A) or that PCA always improves both accuracy and interpretability (B), when in reality, correlated features can harm model stability and PCA reduces interpretability by transforming features into abstract components.
Detailed technical explanation
How to think about this question
Multicollinearity inflates the standard errors of regression coefficients, making it difficult to assess the individual effect of each predictor on the target variable. In practice, for a churn model, 'number of support tickets' and 'average response time' often share a causal relationship (e.g., more tickets lead to slower response), so removing one feature—typically the one with lower predictive power or higher measurement cost—maintains model parsimony. This approach aligns with the bias-variance tradeoff, where reducing redundant features lowers variance without introducing significant bias.
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 AI Associate 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.
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 AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove one of the correlated features — Removing one of the highly correlated features (D) is the best action because multicollinearity between 'number of support tickets' and 'average response time' can inflate the variance of coefficient estimates, making the model unstable and harder to interpret. By dropping one feature, you reduce redundancy without significant information loss, preserving both accuracy and interpretability in a linear or tree-based model.
What should I do if I get this AI Associate 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
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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