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Question 574 of 1,755

Modeling →easyMultiple SelectObjective-mapped

Appropriate Feature Selection Techniques for Linear Regression

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 data scientist is performing feature selection for a linear regression model. Which TWO methods are appropriate? (Choose TWO.)

A
Lasso (L1) regularization
Lasso can zero out feature coefficients, effectively selecting features.
B
Ridge (L2) regularization
Why wrong: Ridge shrinks coefficients but does not set them to zero.
C
t-distributed stochastic neighbor embedding (t-SNE)
Why wrong: t-SNE is for dimensionality reduction for visualization, not feature selection.
D
Forward selection
Forward selection iteratively adds features based on performance.
E
Principal component analysis (PCA)
Why wrong: PCA creates new features, does not select original ones.

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

✓

Lasso (L1) regularization

Both Lasso (L1) regularization and forward selection are appropriate feature selection methods for linear regression. Lasso adds an L1 penalty that shrinks some coefficients exactly to zero, effectively selecting features. Forward selection iteratively adds features based on improvement to the model. Option B (Ridge) is incorrect because L2 regularization shrinks coefficients but does not set them to zero. Option C (t-SNE) is a nonlinear dimensionality reduction technique for visualization, not feature selection. Option E (PCA) creates new components, but does not select original features.

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 (L1) regularization
Why this is correct
Lasso can zero out feature coefficients, effectively selecting features.
Related concept
OSPF neighbours must agree on key parameters.
✗
Ridge (L2) regularization
Why it's wrong here
Ridge shrinks coefficients but does not set them to zero.
✗
t-distributed stochastic neighbor embedding (t-SNE)
Why it's wrong here
t-SNE is for dimensionality reduction for visualization, not feature selection.
✓
Forward selection
Why this is correct
Forward selection iteratively adds features based on performance.
Related concept
OSPF neighbours must agree on key parameters.
✗
Principal component analysis (PCA)
Why it's wrong here
PCA creates new features, does not select original ones.

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 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: Lasso (L1) regularization — Both Lasso (L1) regularization and forward selection are appropriate feature selection methods for linear regression. Lasso adds an L1 penalty that shrinks some coefficients exactly to zero, effectively selecting features. Forward selection iteratively adds features based on improvement to the model. Option B (Ridge) is incorrect because L2 regularization shrinks coefficients but does not set them to zero. Option C (t-SNE) is a nonlinear dimensionality reduction technique for visualization, not feature selection. Option E (PCA) creates new components, but does not select original features.

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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