- A
Lasso regularization
Why wrong: Lasso is a regularization technique for feature selection in models, not a dimensionality reduction method for matrices.
- B
Mutual information feature selection
Why wrong: Mutual information scores features but does not perform reduction by transforming the feature space.
- C
Principal Component Analysis (PCA)
PCA reduces dimensions by projecting onto principal components that capture maximum variance.
- D
t-Distributed Stochastic Neighbor Embedding (t-SNE)
t-SNE reduces high-dimensional data to 2 or 3 dimensions for visualization, preserving local structure.
- E
Singular Value Decomposition (SVD)
SVD factorizes the matrix into lower-rank approximations, commonly used in collaborative filtering.
Question 912 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning team is building a product…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 machine learning team is building a product recommendation system. They have a dataset with millions of users and thousands of products. The team wants to reduce the dimensionality of the user-product interaction matrix while preserving as much variance as possible. Which THREE techniques are appropriate for dimensionality reduction? (Choose THREE.)
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
✓
Principal Component Analysis (PCA)
PCA, SVD, and t-SNE are common dimensionality reduction techniques. PCA and SVD are linear methods that maximize variance. t-SNE is non-linear and good for visualization. Lasso is for feature selection, not matrix factorization. Mutual information is for feature selection, not reduction.
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.
- ✗
Lasso regularization
Why it's wrong here
Lasso is a regularization technique for feature selection in models, not a dimensionality reduction method for matrices.
- ✗
Mutual information feature selection
Why it's wrong here
Mutual information scores features but does not perform reduction by transforming the feature space.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensions by projecting onto principal components that capture maximum variance.
Related concept
OSPF neighbours must agree on key parameters.
- ✓
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why this is correct
t-SNE reduces high-dimensional data to 2 or 3 dimensions for visualization, preserving local structure.
Related concept
OSPF neighbours must agree on key parameters.
- ✓
Singular Value Decomposition (SVD)
Why this is correct
SVD factorizes the matrix into lower-rank approximations, commonly used in collaborative filtering.
Related concept
OSPF neighbours must agree on key parameters.
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
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 MLA-C01 OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
OSPF neighbours must agree on key parameters.
What is the correct answer to this question?
The correct answer is: Principal Component Analysis (PCA) — PCA, SVD, and t-SNE are common dimensionality reduction techniques. PCA and SVD are linear methods that maximize variance. t-SNE is non-linear and good for visualization. Lasso is for feature selection, not matrix factorization. Mutual information is for feature selection, not reduction.
What should I do if I get this MLA-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 MLA-C01 OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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