AI0-001 Machine Learning and Deep Learning Practice Question
A team is building a recommendation system using collaborative filtering. They have a sparse user-item matrix. Which technique should they use to handle the sparsity and improve recommendations?
⚠ Common exam trap
CompTIA often tests the misconception that k-nearest neighbors (k-NN) is the go-to for collaborative filtering, but candidates fail to recognize that k-NN's performance collapses under high sparsity, whereas matrix factorization explicitly models latent factors to overcome this.
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
✓
Matrix factorization
Matrix factorization (B) is the correct technique because it decomposes the sparse user-item matrix into lower-dimensional latent factor matrices, effectively capturing underlying patterns and filling in missing entries. This directly addresses sparsity by learning dense representations that generalize beyond observed interactions, which is a core strength in collaborative filtering for recommendation systems.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Association rule mining
Why it's wrong here
Association rule mining finds frequent itemsets, not suited for sparse user-item matrices.
- ✓
Matrix factorization
Why this is correct
Matrix factorization reduces dimensionality and captures latent features, effectively handling sparsity.
- ✗
k-nearest neighbors
Why it's wrong here
k-NN relies on similarity measures that may be inaccurate with sparse data.
- ✗
Content-based filtering
Why it's wrong here
Content-based filtering uses item attributes, not user-item interactions, and does not directly address matrix sparsity.
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.