- 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 transforms the original high-dimensional feature space into a set of orthogonal principal components, ordered by the amount of variance they capture. By selecting the top 50 components that explain 95% of the variance, the team effectively reduces the feature set from over 500 dimensions while preserving the most informative structure in the data, directly addressing the goals of lowering computational cost and mitigating overfitting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA AI+ exam questions often test the distinction between dimensionality reduction techniques (PCA) and feature selection methods (Lasso, correlation-based selection) or visualization tools (t-SNE), expecting candidates to recognize that PCA is the only option that explicitly reduces dimensionality while preserving maximum variance in a way that is suitable for downstream modeling.
Detailed technical explanation
How to think about this question
PCA works by computing the eigenvectors and eigenvalues of the data's covariance matrix, where each eigenvector (principal component) represents a direction of maximum variance, and the corresponding eigenvalue quantifies the variance explained by that component. In practice, the number of components is chosen based on the cumulative explained variance ratio, often targeting 95% to balance dimensionality reduction with information retention. A subtle behavior is that PCA assumes linear relationships and is sensitive to feature scaling, so standardizing the data (e.g., using Z-scores) is a critical prerequisite before applying PCA.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
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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 — Read the scenario before looking for a memorised answer..
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 transforms the original high-dimensional feature space into a set of orthogonal principal components, ordered by the amount of variance they capture. By selecting the top 50 components that explain 95% of the variance, the team effectively reduces the feature set from over 500 dimensions while preserving the most informative structure in the data, directly addressing the goals of lowering computational cost and mitigating overfitting.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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