Exam AWS Certified Machine Learning - Specialty topic 1 question 276 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 276
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables. All the variables are numeric.

The model accuracy for training and validation is low. The model's processing time is affected by high latency. The data science team needs to increase the accuracy of the model and decrease the processing time.

What should the data science team do to meet these requirements?

A. Create new features and interaction variables.
B. Use a principal component analysis (PCA) model.
C. Apply normalization on the feature set.
D. Use a multiple correspondence analysis (MCA) model.

Show Suggested Answer

Suggested Answer: B 🗳️

by usamazubairi at Dec. 14, 2023, 3:30 p.m.

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Comments

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a67b3be

1 month, 1 week ago

Selected Answer: B

Indeed PCA is not "model", it is a technique. B should be : Use PCA for dimensionality reduction.

upvoted 1 times

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taustin2

1 year ago

Selected Answer: B

It's PCA. It's not MCA because all the values are numeric and not categorical.

upvoted 1 times

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xiaoeason

1 year ago

Selected Answer: B

need to reduce the dimension of features in order to enhance accuracy on train and test data since # of features are huge.

upvoted 1 times

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usamazubairi

1 year ago

B. Use a principal component analysis (PCA) model. This is because PCA can help to reduce the number of variables while preserving the most important information, which can help to improve the accuracy of the model and reduce the processing time.

upvoted 3 times

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