- A
Keep all features but apply L1 regularization (Lasso) in the model to automatically reduce coefficients to zero.
Why wrong: While L1 regularization can reduce feature impact, it does not reduce the number of features in the data pipeline, and computational cost remains high during training.
- B
Apply t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce the feature space to 50 dimensions.
Why wrong: t-SNE is used for visualization in low dimensions (2-3) and is non-linear, making it unsuitable for later model training on new data.
- C
Select only features that have a high correlation with the target variable, discarding all others.
Why wrong: Correlation-based selection may miss features with non-linear relationships or interactions, and discarding too many features could lose information.
- D
Use Principal Component Analysis (PCA) to reduce the feature space to the top 50 principal components that explain 95% of the variance.
PCA efficiently reduces dimensionality while retaining most variance, and the components can be used in downstream models.
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 retail company is building a recommendation system to suggest products to customers based on their purchase history. The data engineering team has collected data from point-of-sale systems, online browsing logs, and customer reviews. After cleaning the data, they notice that the feature set has over 500 dimensions, leading to high computational costs and potential overfitting. They need to reduce dimensionality while preserving as much variance as possible for the model. The team is considering various techniques. Which approach should they take to achieve this goal most effectively?
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
Use Principal Component Analysis (PCA) to reduce the feature space to the top 50 principal components that explain 95% of the variance.
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that projects data onto a lower-dimensional subspace while maximizing variance. It is well-suited for reducing a large number of correlated features. t-SNE is primarily for visualization and does not produce a transformation that can be applied to new data easily. Feature selection based on correlation may discard useful interactions. Keeping all features and using regularization would still require full feature set during training and may not reduce dimensionality in the pipeline.
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.
- ✗
Keep all features but apply L1 regularization (Lasso) in the model to automatically reduce coefficients to zero.
Why it's wrong here
While L1 regularization can reduce feature impact, it does not reduce the number of features in the data pipeline, and computational cost remains high during training.
- ✗
Apply t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce the feature space to 50 dimensions.
Why it's wrong here
t-SNE is used for visualization in low dimensions (2-3) and is non-linear, making it unsuitable for later model training on new data.
- ✗
Select only features that have a high correlation with the target variable, discarding all others.
Why it's wrong here
Correlation-based selection may miss features with non-linear relationships or interactions, and discarding too many features could lose information.
- ✓
Use Principal Component Analysis (PCA) to reduce the feature space to the top 50 principal components that explain 95% of the variance.
Why this is correct
PCA efficiently reduces dimensionality while retaining most variance, and the components can be used in downstream models.
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 Models and Data Engineering — This question tests AI Models and Data Engineering — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Use Principal Component Analysis (PCA) to reduce the feature space to the top 50 principal components that explain 95% of the variance. — Principal Component Analysis (PCA) is a linear dimensionality reduction technique that projects data onto a lower-dimensional subspace while maximizing variance. It is well-suited for reducing a large number of correlated features. t-SNE is primarily for visualization and does not produce a transformation that can be applied to new data easily. Feature selection based on correlation may discard useful interactions. Keeping all features and using regularization would still require full feature set during training and may not reduce dimensionality in the pipeline.
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: Jun 23, 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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