Quick Answer
The best algorithm for sparse collaborative filtering with 99% missing data is matrix factorization, such as Singular Value Decomposition (SVD). This approach excels because it learns latent factors that capture underlying user-item interactions, effectively generalizing patterns from the observed entries rather than relying on explicit pairwise similarities that break down under extreme sparsity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of when to choose dimensionality reduction over memory-based methods like k-NN or item-based collaborative filtering, which fail when most ratings are unobserved. A common trap is selecting association rules or clustering, but matrix factorization’s ability to decompose the sparse user-item matrix into lower-dimensional representations makes it uniquely suited for accurate predictions. Memory tip: think “SVD for sparse voids”—when data is 99% empty, factorize to fill the gaps.
This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is building a recommender system using collaborative filtering. The dataset is sparse (99% missing values). Which algorithm is best suited?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Random Forest
Why wrong: Random Forest does not handle missing values well.
K-Nearest Neighbors
Why wrong: KNN struggles with sparsity.
Matrix Factorization (e.g., SVD)
Matrix factorization works well on sparse data.
Hidden Markov Model
Why wrong: HMM is for time series.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Hidden Markov Model
Why it's wrong here
HMM is for time series.
Common exam traps
Cisco 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.
Detailed technical explanation
Matrix factorization techniques like Singular Value Decomposition (SVD) or Alternating Least Squares (ALS) factorize the user-item matrix into two lower-rank matrices (user and item latent factors), optimizing a loss function that only considers observed entries, thus naturally handling sparsity. In practice, regularization is applied to prevent overfitting, and algorithms like ALS are particularly effective for implicit feedback datasets (e.g., clicks, views) common in real-world recommender systems. A subtle behavior is that matrix factorization can capture temporal dynamics if extended with time-aware factors, but standard SVD assumes static preferences.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a recommendation system using collaborative filtering. The dataset contains implicit feedback (clicks) from users on items. Which algorithm is best suited for this scenario?
easyWhy B: Alternating Least Squares (ALS) is designed for implicit feedback datasets in collaborative filtering. Option A is wrong because SVD is for explicit ratings. Option C is wrong because K-means is clustering, not recommendation. Option D is wrong because Linear Regression is for supervised regression, not recommendation.
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Last reviewed: Jun 24, 2026
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.
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