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.








 

Suggested Answer: B

Community Answer: B




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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