AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for a regression model. The dataset contains 100 features, some of which are highly correlated. To improve model performance and reduce overfitting, which TWO techniques should the data scientist apply? (Select TWO)
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
Feature selection reduces the number of features, and dimensionality reduction (e.g., PCA) handles multicollinearity, both helping to reduce overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Feature selection
Why this is correct
Correct: selecting relevant features reduces noise and overfitting.
- ✓
Dimensionality reduction (e.g., PCA)
Why this is correct
Correct: PCA reduces correlated features and can improve model generalization.
- ✗
Data augmentation
Why it's wrong here
Data augmentation is not typically used for tabular regression data.
- ✗
Adding more hidden layers to the neural network
Why it's wrong here
Adding complexity can increase overfitting, not reduce it.
- ✗
Increasing the learning rate
Why it's wrong here
Learning rate is a hyperparameter, not a technique to reduce overfitting from correlated features.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.