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Amazon MLS-C01 Exam - Topic 5 Question 74 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 74
Topic #: 5
[All MLS-C01 Questions]

A finance company has collected stock return data for 5.000 publicly traded companies. A financial analyst has a dataset that contains 2.000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.

Which solution will meet these requirements with the LEAST operational overhead?

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Suggested Answer: B, D

https://www.redhat.com/en/topics/devops/what-is-blue-green-deployment

by Georgene at Apr 25, 2023, 10:43 PM

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Cecily
5 months ago
I disagree, C could be useful for quick insights without much hassle.
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Launa
6 months ago
Surprised that no one mentioned feature selection techniques!
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Shawna
6 months ago
A linear model? Really? Seems too simplistic for this data.
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Samira
6 months ago
I think B is solid too, random forests are powerful!
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Cammy
6 months ago
Option D seems like the best choice for low overhead.
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Hubert
6 months ago
I recall that using Data Wrangler can simplify the workflow, but I'm uncertain if it provides the best feature importance compared to the other options.
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Marla
6 months ago
I practiced a similar question about feature selection, and I feel like the random forest method might be more robust, but it could be more complex to set up.
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Merrilee
6 months ago
I think using Amazon SageMaker Autopilot could be a good choice since it automates a lot of the process, but I need to double-check how it handles feature importance.
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Lou
7 months ago
I remember studying about feature importance in regression models, but I'm not sure which method has the least overhead.
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Merlyn
7 months ago
Hmm, I'm a bit unsure about this. I know the Orchestrator is some kind of management component, but I'm not sure if it's specifically a load balancer or network switch. I'll have to think this through a bit more.
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Benton
7 months ago
Okay, I've got this. Collecting patient satisfaction data is a common way to evaluate medical students' performance and bedside manner. That's got to be the right answer here.
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Loren
11 months ago
Ah, the age-old question: linear or random forest? It's like choosing between a cup of coffee or a Red Bull - both will get the job done, just depends how much of a caffeine kick you want.
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Tiffiny
10 months ago
Sounds like a plan, it's always good to try different models to see which one performs better.
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Yoko
10 months ago
That's a good point, overfitting can be a problem with random forest. Maybe start with linear regression and then try random forest to compare results.
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Lou
11 months ago
True, random forest can handle non-linear relationships better, but it may overfit the data.
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Fallon
11 months ago
Linear regression is simpler and easier to interpret, but random forest may capture more complex relationships in the data.
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Hector
11 months ago
Options A and B are like the finance equivalent of rock-paper-scissors. I'd go with whichever one makes the most intuitive sense to me.
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Nieves
11 months ago
Option D with Amazon SageMaker Autopilot sounds like the easiest solution, but I'm curious to see how the Clarify report compares to the other methods. Might be worth exploring if I'm feeling lazy.
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Leonora
11 months ago
Let's give Autopilot a try and see how the Clarify report stacks up against the other options. It could save us a lot of time and effort.
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Pok
11 months ago
I agree, the Clarify report from Autopilot will give us valuable insights into the most predictive features for stock returns.
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Corinne
11 months ago
Option D with Amazon SageMaker Autopilot is definitely the way to go. It's the easiest and most efficient method.
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Erin
11 months ago
Option C looks interesting, but I'm not familiar with the quick model visualization in SageMaker. I'd need to do some research to see how reliable the feature importance scores are for this use case.
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Detra
10 months ago
I agree, Option D with Amazon SageMaker Autopilot could be a good option. The Clarify report could help identify the most predictive features.
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Gerri
10 months ago
I think Option B could also be effective. Gini importance scores from random forest regression might provide valuable insights.
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Margart
11 months ago
Option A seems like a solid choice. Using linear regression to rank coefficient values could help identify the top attributes.
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Avery
12 months ago
I'm leaning towards Option B. Random forests can handle nonlinear relationships and the Gini importance scores should give me a good sense of the top predictive features.
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Kirk
11 months ago
I agree, Option B seems like the best option for identifying the most valuable attributes for predicting stock returns.
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Stevie
11 months ago
Gini importance scores can definitely help identify the top predictive features.
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Lucille
11 months ago
Option B sounds like a good choice. Random forests are great for handling nonlinear relationships.
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Holley
12 months ago
Option A seems like a good starting point. Linear regression is a classic approach for this type of problem, and ranking the coefficient values is a straightforward way to identify the most important features.
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Corrie
1 year ago
That's a valid point, Eun. But I still think the automation and detailed report from Autopilot in option D would save time and effort.
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Eun
1 year ago
I disagree, I believe option A is the way to go. Using linear regression and ranking coefficient values seems like a straightforward approach.
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Corrie
1 year ago
I think option D is the best choice because Autopilot can automate the process and provide a detailed report on the most predictive features.
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