Amazon Exam MLS-C01 Topic 3 Question 81 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 81
Topic #: 3

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

AClassification month-to-month using supervised learning of the 200 categories based on claim contents.

BReinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

CForecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

DClassification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Show Suggested Answer

Suggested Answer: D

by Casie at Oct 21, 2023, 03:24 AM

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Fabiola

3 hours ago

I disagree, I believe option C) is more suitable as it involves forecasting using claim IDs and timestamps to predict the number of claims in each category.

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Cherri

3 hours ago

Option C seems the most appropriate here. Forecasting using the claim IDs and timestamps to predict the number of claims in each category is the best way to tackle this problem.

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Stefania

5 days ago

I think option A) is the best choice because it uses supervised learning to classify the 200 categories based on claim contents.

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