This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Logistic regression
Logistic regression models the probability of a binary outcome using a logistic function.
Principal Component Analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique, not a classification algorithm.
Linear regression
Why wrong: Linear regression is for regression problems, not binary classification.
K-means clustering
Why wrong: K-means is an unsupervised clustering algorithm, not for binary classification.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
Cisco 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.
Detailed technical explanation
Logistic regression uses maximum likelihood estimation (MLE) to find coefficients that best separate the classes, and its output is a probability between 0 and 1, which is then thresholded (typically at 0.5) to assign the class label. In real-world churn prediction, features like contract length often have a non-linear relationship with churn, so adding polynomial terms or interaction effects can improve model performance while retaining interpretability. The algorithm also provides odds ratios, which are valuable for business stakeholders to understand the impact of each feature on churn likelihood.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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.
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