AI0-001 AI Models and Data Engineering Practice Question
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?
⚠ Common exam trap
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.
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
✓
Feature combination
'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.
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 converts categorical values into numeric representations, such as one-hot or ordinal codes; it does not derive arithmetic relationships between continuous fields. Encoding is the right technique when a model cannot consume raw text or categorical labels and those values must be transformed into numbers.
- ✗
Feature scaling
Why it's wrong here
Dividing income by debt creates a new derived attribute from existing columns; scaling instead transforms each feature onto a common range, such as min-max or standardisation, without combining variables. Scaling would be the right technique when features differ wildly in magnitude and distance-based algorithms are being trained.
- ✗
Feature selection
Why it's wrong here
Feature selection chooses a subset of existing columns and discards the rest; it never constructs a new ratio from two source fields. Selection is the correct technique when the dataset contains redundant or irrelevant predictors and the goal is to reduce dimensionality while retaining the original variables.
- ✓
Feature combination
Why this is correct
Feature combination creates new attributes by arithmetically relating two or more existing variables, here dividing income by debt to produce income_to_debt_ratio. This satisfies the stem's constraint that the engineer derived a single ratio from separate income and debt fields, rather than transforming one column or selecting a subset.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.