Quick Answer
The correct answer is L1 regularization (Lasso) and Recursive Feature Elimination (RFE). L1 regularization works by adding a penalty equal to the absolute value of the coefficient magnitudes, which forces some coefficients to become exactly zero, thereby removing irrelevant features automatically during model training. RFE, in contrast, is a wrapper method that iteratively trains a model, ranks features by importance, and prunes the weakest ones until the desired number remains. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of embedded versus wrapper feature selection techniques—a common trap is confusing L1 with L2 regularization, which shrinks coefficients but never zeros them out. Remember the mnemonic: “Lasso Leaves Zeroes, RFE Removes Features.”
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.
Which TWO techniques are commonly used for feature selection in machine learning? (Choose 2)
Principal Component Analysis (PCA)
Why wrong: PCA is feature extraction, not selection.
SMOTE
Why wrong: SMOTE is for class imbalance.
L1 regularization (Lasso)
Lasso can zero out coefficients.
Dropout
Why wrong: Dropout is for regularization, not feature selection.
Recursive Feature Elimination (RFE)
RFE selects features by importance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
L1 regularization (Lasso)
L1 regularization (Lasso) is correct because it adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero, effectively performing feature selection by removing irrelevant features from the model. This makes it a built-in feature selection technique within the training process.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is feature extraction, not selection.
SMOTE
Why it's wrong here
SMOTE is for class imbalance.
L1 regularization (Lasso)
Why this is correct
Lasso can zero out coefficients.
Related concept
Read the scenario before looking for a memorised answer.
Dropout
Why it's wrong here
Dropout is for regularization, not feature selection.
Recursive Feature Elimination (RFE)
Why this is correct
RFE selects features by importance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
CompTIA often tests the distinction between dimensionality reduction (PCA) and feature selection, where candidates mistakenly think PCA selects original features rather than creating new ones.
Detailed technical explanation
L1 regularization works by adding the sum of absolute weights to the loss function, which creates a non-differentiable point at zero for each coefficient, leading to sparse solutions where many coefficients become exactly zero. In practice, this is particularly useful in high-dimensional datasets (e.g., genomics or text classification) where the number of features exceeds the number of samples, as it automatically identifies the most predictive features while ignoring noise. Recursive Feature Elimination (RFE) works by training a model, ranking features by importance (e.g., coefficients or feature_importances_), removing the least important feature, and repeating the process recursively until a desired number of features remains.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: L1 regularization (Lasso) — L1 regularization (Lasso) is correct because it adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero, effectively performing feature selection by removing irrelevant features from the model. This makes it a built-in feature selection technique within the training process.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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