AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist is using an ensemble method to combine multiple models. Which three statements about bagging (Bootstrap Aggregating) are true? (Select THREE.)
⚠ Common exam trap
A common trap is confusing variance reduction (bagging) with bias reduction (boosting), leading candidates to incorrectly select option D.
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
✓
It reduces variance without increasing bias
Bagging reduces variance by training models on different bootstrap samples of the data and averaging their predictions. Since each model is trained independently on a random sample with replacement, the ensemble's variance decreases without introducing additional bias, as the expected prediction remains unbiased. This is a key property that distinguishes bagging from boosting, which reduces both bias and variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It requires the base models to be of different types
Why it's wrong here
Bagging typically uses the same type of base model (e.g., all decision trees).
- ✓
It reduces variance without increasing bias
Why this is correct
Bagging averages predictions from models trained on bootstrap samples, reducing variance while bias remains similar.
- ✓
It can be used with decision trees to create random forests
Why this is correct
Random forests use bagging with additional random feature selection.
- ✗
It reduces the error by combining weak learners
Why it's wrong here
That describes boosting; bagging combines strong or unstable models to reduce variance.
- ✓
It trains models independently on bootstrap samples
Why this is correct
Each model is trained on a separate bootstrap sample, and training is parallel.
About these practice questions
One of 754 original AI0-001 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.