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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 363 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 363
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.

The data scientist wants to understand the variance in the data along various directions in the feature space.

Which solution will meet these requirements?

  • A. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.
  • B. Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.
  • C. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.
  • D. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.
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Suggested Answer: C 🗳️
by MultiCloudIronMan at Oct. 30, 2024, 10:38 p.m.
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spinatram
1 week, 5 days ago
https://aws.amazon.com/blogs/machine-learning/detect-multicollinearity-target-leakage-and-feature-correlation-with-amazon-sagemaker-data-wrangler/ Not sure about A or C, but I think it is C
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MultiCloudIronMan
2 weeks, 1 day ago
Selected Answer: C
This approach allows the data scientist to refine the training dataset by selecting the most predictive variables and understanding the variance in the data effectively.
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