- A
Increase the number of training epochs
Why wrong: More epochs can lead to overfitting.
- B
Use k-fold cross-validation only
Why wrong: Cross-validation evaluates performance, but does not directly reduce overfitting or improve interpretability.
- C
Feature selection using correlation analysis
Removing irrelevant or redundant features reduces overfitting and makes the model simpler to interpret.
- D
Principal Component Analysis (PCA)
Why wrong: PCA reduces dimensionality but creates uninterpretable components.
Question 353 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.
A machine learning team is building a model to predict house prices. They have a dataset with 50 features, including square footage, number of bedrooms, and neighborhood. They want to reduce overfitting and improve model interpretability. Which technique should they apply?
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
✓
Feature selection using correlation analysis
Feature selection using correlation analysis reduces overfitting by removing redundant or irrelevant features, which simplifies the model and improves interpretability. With 50 features, many may be correlated (e.g., square footage and number of bedrooms), and eliminating those reduces noise and the risk of learning spurious patterns.
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 training epochs
Why it's wrong here
More epochs can lead to overfitting.
- ✗
Use k-fold cross-validation only
Why it's wrong here
Cross-validation evaluates performance, but does not directly reduce overfitting or improve interpretability.
- ✓
Feature selection using correlation analysis
Why this is correct
Removing irrelevant or redundant features reduces overfitting and makes the model simpler to interpret.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but creates uninterpretable components.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse dimensionality reduction (PCA) with feature selection, but PCA creates uninterpretable components, while correlation-based selection retains original feature names for interpretability.
Detailed technical explanation
How to think about this question
Correlation analysis typically uses Pearson correlation coefficient to measure linear relationships between features and the target, and between features themselves. A common practice is to remove one feature from any pair with a correlation above 0.9 (or 0.95) to mitigate multicollinearity, which can destabilize coefficient estimates in linear models. In real-world scenarios, domain knowledge is often combined with correlation thresholds to avoid discarding features that are important despite high correlation.
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
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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: Feature selection using correlation analysis — Feature selection using correlation analysis reduces overfitting by removing redundant or irrelevant features, which simplifies the model and improves interpretability. With 50 features, many may be correlated (e.g., square footage and number of bedrooms), and eliminating those reduces noise and the risk of learning spurious patterns.
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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