MLS-C01 Modeling Practice Question
A data scientist is building a recommender system using collaborative filtering. The dataset is sparse (99% missing values). Which algorithm is best suited?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that K-Nearest Neighbors (KNN) is the default for collaborative filtering, but the trap here is that extreme sparsity (99% missing) makes pairwise similarity calculations unreliable, whereas matrix factorization explicitly models latent factors to overcome data sparsity.
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 (e.g., SVD)
Matrix factorization (e.g., SVD) is best suited for sparse collaborative filtering because it learns latent factors that capture underlying user-item interactions, effectively handling the 99% missing values by generalizing patterns rather than relying on explicit pairwise similarities. Unlike memory-based methods, it decomposes the sparse user-item matrix into lower-dimensional representations, enabling accurate predictions even when most entries are unobserved.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Random Forest
Why it's wrong here
Random Forest does not handle missing values well.
- ✗
K-Nearest Neighbors
Why it's wrong here
KNN struggles with sparsity.
- ✓
Matrix Factorization (e.g., SVD)
Why this is correct
Matrix factorization works well on sparse data.
- ✗
Hidden Markov Model
Why it's wrong here
HMM is for time series.
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Written by Johnson Ajibi, MSc IT Security
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.