Quick Answer
The answer is linear regression, as it is the most appropriate interpretable regression algorithm for a regression problem with 50 features and 1 million training examples that fits in memory. Linear regression provides a clear, interpretable model by assigning a coefficient to each feature, allowing you to directly understand the relationship between inputs and the target variable, and it scales efficiently to large datasets through closed-form solutions or stochastic gradient descent. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to balance interpretability with scalability, often trapping candidates who choose XGBoost for its performance but overlook its black-box nature, or k-NN for its simplicity despite its high inference cost. A key memory tip: when you need to explain why a prediction was made to a stakeholder, think "linear" for clear coefficients, not "complex" for hidden trees.
This MLS-C01 practice question tests your understanding of modeling. 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 data scientist needs to choose an algorithm for a regression problem with 50 features and 1 million training examples. The model must be interpretable and the training data fits in memory. Which algorithm is most appropriate?
Principal Component Analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique, not a regression algorithm.
Linear regression
Linear regression is interpretable and efficient for large datasets.
XGBoost
Why wrong: XGBoost is a tree ensemble, less interpretable and may require more tuning.
k-Nearest Neighbors
Why wrong: k-NN is computationally expensive for large datasets and not interpretable.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Linear regression
Option A is correct because linear regression is interpretable, scales well to large datasets, and is suitable for regression. Option B is wrong because XGBoost is less interpretable. Option C is wrong because k-NN is computationally expensive at inference and not interpretable. Option D is wrong because PCA is dimensionality reduction, not a regression algorithm.
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.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not a regression algorithm.
Linear regression
Why this is correct
Linear regression is interpretable and efficient for large datasets.
Related concept
OSPF neighbours must agree on key parameters.
XGBoost
Why it's wrong here
XGBoost is a tree ensemble, less interpretable and may require more tuning.
k-Nearest Neighbors
Why it's wrong here
k-NN is computationally expensive for large datasets and not interpretable.
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
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
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 — study guide chapter
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FAQ
Modeling — This question tests Modeling — OSPF neighbours must agree on key parameters..
The correct answer is: Linear regression — Option A is correct because linear regression is interpretable, scales well to large datasets, and is suitable for regression. Option B is wrong because XGBoost is less interpretable. Option C is wrong because k-NN is computationally expensive at inference and not interpretable. Option D is wrong because PCA is dimensionality reduction, not a regression algorithm.
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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