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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Dottie

2 months ago

Ah, the classic 'Which model should I use?' question. I bet the exam writer is having a good laugh at our expense right now.

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Hubert

24 days ago

D) Classification 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.

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Deonna

26 days ago

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

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Lavonda

28 days ago

A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.

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Lera

2 months ago

Reinforcement learning? Really? I think the Data Scientist is looking for a more straightforward solution here, not some kind of game-playing algorithm.

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Loise

15 days ago

D) Classification 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.

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Shaquana

1 months ago

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

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Mozell

2 months ago

A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.

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Sabra

2 months ago

Hmm, I'm not sure I agree. What if the partial information on claim contents could actually help improve the forecasting model? Option D might be worth considering.

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Devon

18 days ago

I agree, it seems like a good approach to leverage the available data for better predictions.

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Pedro

2 months ago

That's true, using partial information for some categories and forecasting for others could be beneficial.

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Margarita

2 months ago

Option D might be a good choice since it combines classification and forecasting.

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Edelmira

2 months ago

I agree, C is the way to go. Trying to classify all 200 categories with only partial information on claim contents is likely to be very challenging.

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Remedios

1 months ago

I think D could also be a good option, combining classification with forecasting for different categories.

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Nobuko

1 months ago

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

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Stefania

3 months ago

That's a valid point, Fabiola. Option C) could be more accurate in predicting the number of claims month to month.

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Fabiola

3 months 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 months 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

3 months 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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