Amazon Exam MLS-C01 Topic 9 Question 52 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 52
Topic #: 9

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

AThe XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

BThe XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

CThe XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

DThe XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Show Suggested Answer

Suggested Answer: C

by Yuki at May 04, 2022, 12:17 AM

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yak22

3 years ago

It is a clustering problem so it should be D because K-Mean allows to predict cluster/segments which are unknown in this case where in option C k-nearest is used for classification problem to predict class labels for individual data instances.

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