AIF-C01 Fundamentals of AI and ML Practice Question
A data science team needs to choose a machine learning approach for a project that requires predicting customer churn based on historical data. The team has a labeled dataset with 10,000 records and needs to interpret the model's decisions to provide business insights. Which machine learning technique should the team prioritize?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates might choose a powerful but opaque model like a deep neural network, overlooking the explicit requirement for interpretability and the modest dataset size that favors simpler, more explainable ensemble methods.
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
✓
Random forest.
Random forest is the best choice because it handles the classification task of predicting churn (a binary outcome) from a labeled dataset, provides feature importance scores for interpretability, and works well with 10,000 records without overfitting due to its ensemble of decision trees. Its built-in ability to rank input features directly supports the team's need to derive business insights from model 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.
- ✓
Random forest.
Why this is correct
Random forest is supervised and handles labelled tabular data well, and its ensemble of decision trees exposes feature importance values. Those importances give the interpretable business insight the team needs, unlike opaque deep learning or unsupervised clustering.
- ✗
K-means clustering.
Why it's wrong here
K-means is unsupervised, so it cannot use the labelled churn records to learn a churn prediction; it only groups unlabelled points by similarity. It tempts because it is a common customer analytics technique, and would be correct for segmenting customers into clusters without predefined labels.
- ✗
Linear regression.
Why it's wrong here
Linear regression predicts a continuous value, so it cannot output the discrete churn/not-churn class the labelled dataset requires. It tempts because it is genuinely interpretable, and would suit forecasting a numeric target such as predicted customer lifetime value rather than a binary classification outcome.
- ✗
Deep neural network with multiple hidden layers.
Why it's wrong here
A deep neural network with multiple hidden layers produces decisions that cannot be readily explained, failing the requirement to interpret decisions for business insights. It tempts because it handles large, complex datasets well, and would be right where predictive accuracy matters more than transparency.
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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.