Amazon Exam MLS-C01 Topic 5 Question 74 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 74
Topic #: 5

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?

AUse the linear learner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.

BUse random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.

CUse an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick model's feature importance scores.

DUse Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.

Show Suggested Answer

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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Loren

18 days 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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Fallon

2 days 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

23 days 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

24 days 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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Corinne

1 days 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

27 days 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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Margart

21 days 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

1 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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Stevie

9 days ago

Gini importance scores can definitely help identify the top predictive features.

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Lucille

14 days ago

Option B sounds like a good choice. Random forests are great for handling nonlinear relationships.

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Holley

1 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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