AWS Certified Machine Learning - Specialty (MLS-C01) Practice Exam

Correct Answer: C. The model is 86% accurate and the cost incurred by the company as a result of false positives isfiless than the false negatives.

Why Option C is right: In this specific business context, the company states that the cost of churn is far greater than the cost of the incentive. A False Negative (Actual Churn "Yes", Predicted "No") means a customer leaves, which is the highest cost. A False Positive (Actual Churn "No", Predicted "Yes") means giving an incentive to a loyal customer. While this is a small loss, it is much better than losing the customer. Since the model captures most churners and maintains high overall accuracy, it is a viable business solution.

Correct Answer: B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Collaborative filtering is the correct approach for this use case because it predicts user preferences based on similarities between users, not on product content characteristics. The question explicitly states the objective is to "predict which products users would like based on the users' similarity to other users," which is the defining principle of collaborative filtering. Apache Spark ML on Amazon EMR is an appropriate implementation platform for this algorithm, as it provides distributed computing capabilities for large-scale data processing. Collaborative filtering works by identifying user-user or item-item similarities and leveraging the preferences of similar users to make recommendations.Why correct: Collaborative filtering explicitly uses user similarity to make predictions Aligns with stated objective: "based on the users' similarity to other users" Spark ML on EMR supports distributed collaborative filtering implementation Why others are incorrect: A (Content-based filtering): Uses product features/content, not user similarity C (Model-based filtering): A variant of collaborative filtering, but less direct for this use case; the question emphasizes similarity between users D (Combinative filtering): Not a standard ML terminology; likely a distractor