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Fundamentals of AI and MLeasyMultiple ChoiceObjective-mapped

AIF-C01 Fundamentals of AI and ML Practice Question

A startup needs to predict customer churn based on historical data containing labels (churned or not). Which type of machine learning should they use?

⚠ Common exam trap

The AIF-C01 exam often tests the distinction between supervised and unsupervised learning by presenting a scenario with labeled data, where candidates might mistakenly choose unsupervised learning if they overlook the presence of labels.

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

Supervised learning

The startup has labeled historical data (churned or not), which is the defining characteristic of supervised learning. The goal is to learn a mapping from input features to the known output labels to predict churn for new customers. This is a classic classification problem, making supervised learning the correct choice.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Reinforcement learning

    Why it's wrong here

    Reinforcement learning is used for sequential decision-making with rewards, not for label-based prediction.

  • Unsupervised learning

    Why it's wrong here

    Unsupervised learning does not use labeled data, so it cannot predict a specific outcome.

  • Supervised learning

    Why this is correct

    Since the data has labels, supervised learning is appropriate for classification.

  • Semi-supervised learning

    Why it's wrong here

    Semi-supervised learning uses a small labelled dataset to infer labels for a larger unlabelled dataset, but the startup already possesses complete historical labels for every record. The temptation arises because semi-supervised learning is effective when labelling is expensive or scarce, such as in medical imaging where only a few scans are diagnosed. Here, the requirement is supervised learning, which trains directly on the fully labelled churn data to map features to the known binary outcome.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.