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Question 171 of 1,755

Modeling →mediumMultiple ChoiceObjective-mapped

MLS-C01 Modeling Practice Question

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?

A
Principal Component Analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique, not a regression algorithm.
B
Linear regression
Linear regression is interpretable and efficient for large datasets.
C
XGBoost
Why wrong: XGBoost is a tree ensemble, less interpretable and may require more tuning.
D
k-Nearest Neighbors
Why wrong: k-NN is computationally expensive for large datasets and not interpretable.

Full breakdown with real-world context →

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

✓

Linear regression

Linear regression is the most appropriate choice because it is interpretable, handles high-dimensional data with 50 features efficiently, scales well to 1 million training examples that fit in memory, and directly addresses regression. Option A (PCA) is a dimensionality reduction technique, not a regression algorithm. Option C (XGBoost) is powerful for regression but is less interpretable due to its ensemble nature. Option D (k-NN) is non-parametric and computationally expensive at inference time, lacking interpretability.

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.

✗
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

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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Modeling — This question tests Modeling — OSPF neighbours must agree on key parameters..

What is the correct answer to this question?

The correct answer is: Linear regression — Linear regression is the most appropriate choice because it is interpretable, handles high-dimensional data with 50 features efficiently, scales well to 1 million training examples that fit in memory, and directly addresses regression. Option A (PCA) is a dimensionality reduction technique, not a regression algorithm. Option C (XGBoost) is powerful for regression but is less interpretable due to its ensemble nature. Option D (k-NN) is non-parametric and computationally expensive at inference time, lacking interpretability.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

OSPF neighbours must agree on key parameters.

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