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 data engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with 100 features. To improve model performance and reduce training time, the engineer decides to perform feature selection. Which two techniques are appropriate for this task? (Select TWO).
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is an appropriate feature selection technique because it iteratively removes the least important features based on a model's feature importance scores or coefficients, directly reducing the feature count from 100 to a smaller subset. This improves model performance by eliminating irrelevant or redundant features and reduces training time by decreasing dimensionality.
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.
✗
Normalization
Why it's wrong here
Normalization scales features, does not select them.
✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE selects features by removing the least important ones iteratively.
Related concept
Read the scenario before looking for a memorised answer.
✓
L1 Regularization
Why this is correct
L1 regularization adds penalty that sets some feature coefficients to zero, effectively selecting features.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-Hot Encoding
Why it's wrong here
One-hot encoding is for converting categorical variables, not feature selection.
✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but creates new features, not selects original ones.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so candidates mistakenly select PCA thinking it selects features, when it actually transforms them into principal components.
Detailed technical explanation
How to think about this question
RFE works by training a model (e.g., logistic regression or SVM) on the full feature set, ranking features by their importance (e.g., coefficient magnitude or feature_importances_ in tree-based models), then pruning the least important feature(s) and repeating the process on the reduced set until the desired number of features remains. L1 Regularization (Lasso) performs feature selection inherently by adding a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero, effectively removing those features from the model. In practice, RFE is more computationally expensive for high-dimensional data, while L1 regularization is efficient and often preferred for datasets with many features.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Recursive Feature Elimination (RFE) — Recursive Feature Elimination (RFE) is an appropriate feature selection technique because it iteratively removes the least important features based on a model's feature importance scores or coefficients, directly reducing the feature count from 100 to a smaller subset. This improves model performance by eliminating irrelevant or redundant features and reduces training time by decreasing dimensionality.
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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Question Discussion
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