The answer is the built-in Factorization Machines algorithm in Amazon SageMaker. This is the correct choice because Factorization Machines are specifically designed to handle large-scale, high-dimensional sparse data like the 99% missing ratings in this user-item interaction matrix, efficiently modeling pairwise feature interactions through factorized parameters rather than dense embeddings. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s built-in algorithms for recommendation systems, often appearing alongside traps like suggesting linear regression (which cannot capture interaction effects) or deep learning models that overfit on sparse data. A common memory tip: think of Factorization Machines as “sparse data specialists” — they excel where traditional collaborative filtering fails due to missing ratings. Remember the mnemonic “FM for Sparse” to recall that Factorization Machines are the go-to for recommendation tasks with sparse, high-cardinality datasets.
MLS-C01 Modeling Practice Question
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 startup is building a recommendation system for an e-commerce platform using collaborative filtering. They have a dataset of user-item interactions (ratings) with 1 million users and 100,000 items. The data is sparse (99% missing ratings). They need to train a model on Amazon SageMaker that can handle large-scale sparse data efficiently. Which approach should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the built-in Factorization Machines algorithm in SageMaker
SageMaker's Factorization Machines handle sparse data efficiently and are designed for recommendation tasks.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use PCA to reduce dimensionality and then apply k-nearest neighbors
Why it's wrong here
PCA is not suited for missing values and collaborative filtering.
✓
Use the built-in Factorization Machines algorithm in SageMaker
Why this is correct
Factorization Machines are designed for sparse data and scale well.
Use the built-in XGBoost algorithm with one-hot encoding for user and item IDs
Why it's wrong here
One-hot encoding would create a huge feature space and is inefficient.
✗
Implement a neural network with dense layers using the built-in MXNet framework
Why it's wrong here
Dense layers do not handle sparsity well and may overfit.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.
TExam Day Tips
→Check area mismatch first when OSPF adjacency fails.
→Review passive interfaces when a network is advertised but no neighbour forms.
→Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
Modeling — This question tests Modeling — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Use the built-in Factorization Machines algorithm in SageMaker — SageMaker's Factorization Machines handle sparse data efficiently and are designed for recommendation tasks.
What should I do if I get this MLS-C01 question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 for an e-commerce platform. The data includes user IDs and item IDs. Which SageMaker built-in algorithm is most appropriate?
easy
A.BlazingText
B.XGBoost
✓ C.Factorization Machines
D.Image Classification
Why C: Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional categorical data like user IDs and item IDs. They model pairwise interactions between features (e.g., user-item interactions) using factorized parameters, making them highly effective for collaborative filtering and implicit feedback scenarios in e-commerce.
Variation 2. A company is building a recommendation system for an e-commerce platform. The data includes user-item interactions and features such as user demographics and item categories. Which algorithm would be most appropriate for generating personalized recommendations?
medium
A.XGBoost
✓ B.Factorization Machines
C.k-means clustering
D.Principal Component Analysis (PCA)
Why B: Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional data like user-item interactions. They model pairwise feature interactions (e.g., user demographics × item categories) using factorized parameters, enabling personalized recommendations even when many user-item pairs are unobserved. This makes FM far more effective than tree-based or clustering methods for collaborative filtering and feature-rich recommendation scenarios.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
Question Discussion
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