- A
Ridge regression (L2 regularization)
Why wrong: Ridge shrinks coefficients but does not eliminate irrelevant features.
- B
Linear regression
Why wrong: Linear regression is sensitive to irrelevant features and does not perform feature selection.
- C
Lasso regression (L1 regularization)
Lasso applies L1 penalty, driving coefficients of irrelevant features to zero.
- D
K-Nearest Neighbors
Why wrong: KNN is sensitive to irrelevant features and distance metrics; not ideal.
- E
Random Forest
Random Forest can handle many features and provides feature importance to ignore irrelevant ones.
Question 820 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. 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 needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select TWO)
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 regression (L1 regularization)
Random Forest handles irrelevant features well via feature importance. Lasso (L1) regression performs automatic feature selection by shrinking coefficients to zero.
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.
- ✗
Ridge regression (L2 regularization)
Why it's wrong here
Ridge shrinks coefficients but does not eliminate irrelevant features.
- ✗
Linear regression
Why it's wrong here
Linear regression is sensitive to irrelevant features and does not perform feature selection.
- ✓
Lasso regression (L1 regularization)
Why this is correct
Lasso applies L1 penalty, driving coefficients of irrelevant features to zero.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
K-Nearest Neighbors
Why it's wrong here
KNN is sensitive to irrelevant features and distance metrics; not ideal.
- ✓
Random Forest
Why this is correct
Random Forest can handle many features and provides feature importance to ignore irrelevant ones.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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 AI0-001 OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Lasso regression (L1 regularization) — Random Forest handles irrelevant features well via feature importance. Lasso (L1) regression performs automatic feature selection by shrinking coefficients to zero.
What should I do if I get this AI0-001 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 AI0-001 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 AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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