AI0-001 AI Concepts and Foundations Practice Question
A company wants to use AI to automatically categorize customer support tickets into topics like 'billing', 'technical', 'account'. They have 10,000 labeled examples. Which algorithm is most suitable for this task?
⚠ Common exam trap
CompTIA often tests the distinction between supervised and unsupervised learning, so the trap here is that candidates may confuse clustering (DBSCAN) or dimensionality reduction (PCA) with classification, overlooking that labeled data requires a supervised algorithm like logistic regression.
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 a supervised learning algorithm that models the probability of a categorical outcome based on input features. With 10,000 labeled examples, it can efficiently learn decision boundaries to classify tickets into 'billing', 'technical', or 'account' by using a softmax (multinomial logistic regression) extension for multi-class classification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DBSCAN
Why it's wrong here
DBSCAN groups unlabeled points by density and predicts no class labels, so it cannot map tickets to predefined topics. It suits anomaly detection or discovering clusters in unlabeled data. Supervised classifiers trained on the 10,000 labelled examples are required here.
- ✗
Apriori
Why it's wrong here
Apriori mines frequent itemsets and association rules from transactional data, producing no predictive model for assigning category labels. It suits market-basket analysis. The labelled tickets demand supervised text classification, such as naive Bayes or logistic regression.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA performs dimensionality reduction by projecting features onto principal components; it outputs transformed coordinates, not topic labels. It suits compression or visualisation before modelling. Classification of the labelled tickets requires a supervised algorithm that predicts categories.
- ✓
Logistic regression
Why this is correct
Logistic regression is a supervised classifier that learns a decision boundary from the 10,000 labelled examples, outputting category probabilities. It satisfies the labelled multi-class text classification constraint, though multinomial extension is needed beyond binary billing versus non-billing decisions.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.