MLS-C01 Question #303: Real Exam Question with Answer & Explanation

Question

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?

Options

• ACreate new features and interaction variables.
• BUse a principal component analysis (PCA) model.
• CApply normalization on the feature set.
• DUse a multiple correspondence analysis (MCA) model.

Explanation

The data science team needs to improve accuracy and decrease processing time for a supervised classification model with many numeric variables.

Common mistakes.

• A. Creating new features and interaction variables would increase the dataset's dimensionality, exacerbating high latency and potentially worsening accuracy, contradicting the stated requirements.
• C. Applying normalization scales feature values but does not reduce the number of variables or directly address high latency caused by a large quantity of variables.
• D. Multiple Correspondence Analysis (MCA) is a dimensionality reduction technique specifically designed for categorical variables, making it unsuitable for a dataset where all variables are numeric.

Concept tested. Dimensionality reduction for model performance

Reference. https://learn.microsoft.com/en-us/azure/machine-learning/concept-dimensionality-reduction

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