AI0-001 Machine Learning and Deep Learning Practice Question
A junior data scientist is training a supervised classification model to predict whether a loan applicant will default. The dataset has 40,000 labeled historical records with a clear binary outcome column. The team needs a model that outputs a probability between 0 and 1 for the default class. Which algorithm is the most appropriate choice for this task?
⚠ Common exam trap
The trap here is assuming any algorithm that groups or transforms data can serve as a classifier, when unsupervised methods such as clustering and dimensionality reduction never use the target label.
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
✓
Logistic regression
Logistic regression is designed for binary classification and directly estimates the probability that an observation belongs to the positive class through the sigmoid link function. With abundant labeled data and a clear binary target, it satisfies the supervised learning requirement and delivers interpretable, well-calibrated outputs suitable for credit risk decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Logistic regression
Why this is correct
Logistic regression applies a sigmoid function to a linear combination of features, directly producing a probability between 0 and 1 for a binary outcome. With 40,000 labeled records and a binary target, it fits the supervised classification scenario, trains quickly, and yields interpretable coefficients that can support lending decisions and regulatory review.
- ✗
Principal component analysis
Why it's wrong here
Principal component analysis is a dimensionality reduction technique that projects features onto orthogonal components maximizing variance. It is not a predictive classifier, does not consume the default labels during fitting in a supervised sense, and outputs transformed feature vectors rather than a probability of default for an applicant.
- ✗
Apriori association rule mining
Why it's wrong here
Apriori discovers frequent itemsets and association rules in transactional data, such as market basket analysis. It has no mechanism to model a binary target from tabular applicant features and returns support and confidence metrics for item co-occurrence, not a probability that an individual loan applicant will default.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised algorithm that partitions unlabeled data into k groups based on distance to centroids. It does not use the default outcome labels and produces cluster assignments rather than calibrated probabilities for a binary class, so it cannot answer whether a specific applicant will default.
About these practice questions
One of 962 original AI0-001 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.