The correct answer is feature combination, as the new 'income_to_debt_ratio' is derived by applying an arithmetic operation—division—to two existing features, income and debt. This technique, also known as feature crossing or feature construction, is a core part of feature engineering that creates new predictive signals by combining raw variables to capture interactions or relationships that linear models might miss. On the CompTIA AI+ AI0-001 exam, this concept tests your ability to distinguish between feature selection, extraction, and combination; a common trap is confusing feature combination with feature extraction, which transforms data into a lower-dimensional space rather than merging existing variables. Remember the mnemonic "Combine to Conquer"—when you see a new feature formed by adding, multiplying, or dividing existing columns, it is always a feature combination technique.
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 financial institution is training a risk assessment model. The dataset includes customer credit scores, income, age, and past loan defaults. During feature engineering, a data engineer creates a new feature 'income_to_debt_ratio'. Which type of feature engineering technique is this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Feature combination
Option D is correct because 'income_to_debt_ratio' is created by combining two existing features (income and debt) into a single derived feature. This is a classic example of feature combination (also known as feature crossing or feature construction), where arithmetic operations or logical rules are applied to existing variables to generate new predictive signals. The goal is to capture interactions or relationships that the original features alone may not express linearly.
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.
✗
Feature encoding
Why it's wrong here
Encoding transforms categorical variables into numerical, not a ratio of two features.
✗
Feature scaling
Why it's wrong here
Scaling changes the range of values, not creates new features from multiple variables.
✗
Feature selection
Why it's wrong here
Selection chooses a subset of existing features, not creates new ones.
✓
Feature combination
Why this is correct
Creating a ratio from two continuous variables is a combination technique to capture interaction.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between feature engineering techniques by presenting a derived feature and expecting candidates to recognize it as feature combination rather than confusing it with scaling or encoding.
Detailed technical explanation
How to think about this question
Feature combination often involves domain-specific arithmetic such as ratios, differences, or products, which can expose non-linear relationships to linear models like logistic regression. In practice, tools like Featuretools or manual pandas operations create these derived columns; for risk assessment, income_to_debt_ratio directly models debt burden, a key predictor of default probability. A subtle behavior is that combining features can introduce multicollinearity if the original features are retained, so careful feature selection or regularization (e.g., L1 penalty) may be needed.
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: Feature combination — Option D is correct because 'income_to_debt_ratio' is created by combining two existing features (income and debt) into a single derived feature. This is a classic example of feature combination (also known as feature crossing or feature construction), where arithmetic operations or logical rules are applied to existing variables to generate new predictive signals. The goal is to capture interactions or relationships that the original features alone may not express linearly.
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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