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Amazon Exam MLS-C01 Topic 3 Question 81 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 81
Topic #: 3
[All MLS-C01 Questions]

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?

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Suggested Answer: D

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

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Dottie
25 days 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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Lavonda
20 hours ago
A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.
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Lera
27 days 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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Shaquana
17 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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Mozell
21 days ago
A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.
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Sabra
1 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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Pedro
22 days ago
That's true, using partial information for some categories and forecasting for others could be beneficial.
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Margarita
28 days 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
12 days ago
I think D could also be a good option, combining classification with forecasting for different categories.
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Nobuko
14 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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Stefania
2 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
2 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
2 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
2 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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