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Amazon Exam MLS-C01 Topic 7 Question 21 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 21
Topic #: 7
[All MLS-C01 Questions]

A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.

Which machine learning model type should the Specialist use to accomplish this task?

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Suggested Answer: B

The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) -- answers that need to be predicted -- to train an algorithm. With classification, businesses can answer the following questions:

Will this customer churn or not?

Will a customer renew their subscription?

Will a user downgrade a pricing plan?

Are there any signs of unusual customer behavior?


https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

Contribute your Thoughts:

Gail
5 days ago
This question seems straightforward, but I want to double-check the key points about irrevocable trusts to make sure I don't miss anything.
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Doug
7 days ago
This looks like a straightforward automation process, but I want to make sure I understand the requirements correctly. I'll need to review the details carefully.
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Anglea
16 days ago
I'm not totally sure about this one. The examples seem to cover a range of different entities, from corporations to government agencies to business partnerships. I'll have to think it through carefully.
upvoted 0 times
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Lelia
17 days ago
AirWatch Cloud Connector is for integrating Workspace ONE with cloud-based services, so that's definitely not the right answer here. I think it's either the Secure Email Gateway or the Workspace ONE Access Connector, but I'm not 100% confident.
upvoted 0 times
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