AIF-C01 Fundamentals of AI and ML Practice Question
A company wants to predict customer churn. They have historical data with features like usage minutes, support tickets, contract length. The target is binary: churn/not churn. Which ML algorithm is best suited?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may confuse dimensionality reduction (PCA) or clustering (K-means) with classification, or mistakenly apply linear regression to a binary outcome without recognizing the need for a logistic function.
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 the best choice because it is specifically designed for binary classification tasks like predicting churn (churn/not churn). It models the probability of the target class using a logistic (sigmoid) function, making it interpretable and efficient for this type of supervised learning problem with a categorical outcome.
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 models the probability of a binary outcome using a logistic function.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not a classification algorithm.
- ✗
Linear regression
Why it's wrong here
Linear regression is for regression problems, not binary classification.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised clustering algorithm, not for binary classification.
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This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.