Quick Answer
The answer is Truncated Singular Value Decomposition (SVD). This technique is the most appropriate for dimensionality reduction on sparse data because it directly operates on the sparse matrix without requiring it to be made dense, efficiently decomposing the data into lower-rank approximations while preserving the underlying structure and variance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how different algorithms handle sparse, high-dimensional features like bag-of-words or TF-IDF vectors; a common trap is choosing standard PCA, which fails because it relies on a covariance matrix that becomes computationally prohibitive and memory-intensive for sparse data. Remember that Truncated SVD is essentially PCA for sparse matrices, and a helpful memory tip is to think of it as "SVD that truncates the noise" — it keeps only the top k singular values, making it both fast and effective for text and recommendation system features.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?
t-distributed Stochastic Neighbor Embedding (t-SNE).
Why wrong: t-SNE is for visualization, not general reduction.
Truncated Singular Value Decomposition (SVD).
Truncated SVD works efficiently on sparse matrices.
Linear Discriminant Analysis (LDA).
Why wrong: LDA is supervised and requires labels.
Principal Component Analysis (PCA) using the covariance matrix.
Why wrong: PCA with covariance is not suitable for sparse data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Truncated Singular Value Decomposition (SVD).
Option D is correct because Truncated SVD (e.g., using sklearn's TruncatedSVD or PCA on sparse data via SVD) is designed for sparse matrices and preserves variance. Option A is wrong because PCA with covariance matrix requires dense matrix and is computationally expensive for sparse data. Option B is wrong because t-SNE is for visualization, not for general dimensionality reduction preserving structure. Option C is wrong because LDA is a supervised method and requires labels.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
t-distributed Stochastic Neighbor Embedding (t-SNE).
Why it's wrong here
t-SNE is for visualization, not general reduction.
Truncated Singular Value Decomposition (SVD).
Why this is correct
Truncated SVD works efficiently on sparse matrices.
Related concept
OSPF neighbours must agree on key parameters.
Linear Discriminant Analysis (LDA).
Why it's wrong here
LDA is supervised and requires labels.
Principal Component Analysis (PCA) using the covariance matrix.
Why it's wrong here
PCA with covariance is not suitable for sparse data.
Common exam traps
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
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.
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
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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
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.
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
The correct answer is: Truncated Singular Value Decomposition (SVD). — Option D is correct because Truncated SVD (e.g., using sklearn's TruncatedSVD or PCA on sparse data via SVD) is designed for sparse matrices and preserves variance. Option A is wrong because PCA with covariance matrix requires dense matrix and is computationally expensive for sparse data. Option B is wrong because t-SNE is for visualization, not for general dimensionality reduction preserving structure. Option C is wrong because LDA is a supervised method and requires labels.
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.
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 20, 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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