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Amazon MLS-C01 Exam - Topic 3 Question 81 Discussion

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?
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.
A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.
B) Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
C) Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

Amazon MLS-C01 Exam - 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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Lorean
6 months ago
I didn't realize there were 200 categories, that's a lot to handle!
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Lavera
7 months ago
Wait, reinforcement learning? That seems off for this scenario.
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Gladys
7 months ago
Not so sure about that, D could work too.
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Dana
7 months ago
Totally agree, C is the way to go!
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Layla
7 months ago
I think option C makes the most sense for forecasting.
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Fidelia
7 months ago
Option D seems to combine both classification and forecasting, which might be the best approach given the mixed data we have.
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Octavio
8 months ago
I’m a bit confused about whether to use supervised learning for all categories or just the ones with partial information.
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Quentin
8 months ago
I remember practicing a similar question where we used time series analysis for forecasting. That might be relevant here.
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Dick
8 months ago
I think we should focus on forecasting since we're trying to predict future claims, but I'm not sure if we need to classify first.
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Corazon
8 months ago
This seems straightforward to me. I'd go with option D - use supervised learning for the categories with partial info, and forecasting for the rest. Seems like the most comprehensive approach.
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Osvaldo
8 months ago
Okay, I think I've got a plan. I'd use forecasting for the categories with limited claim content data, and supervised learning for the ones with more details. That should give us the best of both worlds.
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Billye
8 months ago
Hmm, I'm a bit confused. There's a lot of different information here to consider. I'll need to think through the details carefully before deciding on the best approach.
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Stefanie
8 months ago
This looks like a tricky one. I'm thinking a combination of forecasting and supervised learning might be the way to go, but I'm not totally sure.
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Malinda
8 months ago
Hmm, I think I know the answer to this one. Let me double-check the options and make sure I'm on the right track.
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Cecily
8 months ago
Okay, let's see here. The question is asking for two options that can be used to specify a time range. I'm pretty sure "start=" and "end=" are the right answers, but I'll double-check the other options just to be sure.
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Felix
8 months ago
I'm pretty sure most standard health plans cover both routine and clinical eye care. So I'm leaning towards option A.
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Kathrine
8 months ago
I remember that Blue Prism can use AES-256 for credentials, but I'm not sure if it's always the case.
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Ruthann
8 months ago
I remember we discussed the importance of upgrading APIC controllers in a specific order to minimize traffic impact. Should it be done step by step?
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Wai
8 months ago
I'm a bit unsure about this one. I know we need to generate health check logs, but I'm not familiar with the specific process for an SAP HANA on Azure deployment. I'll need to carefully review the options and try to identify the most logical approach.
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Dottie
1 year 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
12 months 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
1 year 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
1 year ago
A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.
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Lera
1 year 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
12 months 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 year 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
1 year ago
A) Classification month-to-month using supervised learning of the 200 categories based on claim contents.
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Sabra
1 year 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
12 months ago
I agree, it seems like a good approach to leverage the available data for better predictions.
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Pedro
1 year ago
That's true, using partial information for some categories and forecasting for others could be beneficial.
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Margarita
1 year ago
Option D might be a good choice since it combines classification and forecasting.
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Edelmira
1 year 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 year ago
I think D could also be a good option, combining classification with forecasting for different categories.
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Nobuko
1 year 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
1 year 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
1 year 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
1 year 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
1 year 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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