Amazon Exam MLS-C01 Topic 3 Question 118 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 118
Topic #: 3

[All MLS-C01 Questions]

[Modeling]

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical

features. The Marketing team has not provided any insight about which features are relevant for churn

prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on

the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide

gap between the training and validation set accuracy.

Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's

needs? (Choose two.)

AAdd L1 regularization to the classifier

BAdd features to the dataset

CPerform recursive feature elimination

DPerform t-distributed stochastic neighbor embedding (t-SNE)

EPerform linear discriminant analysis

Show Suggested Answer

Suggested Answer: A, C

by Gearldine at Jul 09, 2025, 10:14 PM

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Cletus

9 days ago

I think we should add L1 regularization to the classifier.

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Dorothy

10 days ago

I think option C is the way to go. Recursive feature elimination can help the Data Scientist identify the most relevant features for churn prediction and satisfy the Marketing team's needs.

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