MLS-C01 Question #382: Real Exam Question with Answer & Explanation

Question

A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question. A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model. Which solution will meet these requirements?

Options

• APerform one-hot encoding on every possible option for each question of the survey.
• BPerform binning on all the answers each respondent selected for each question.
• CUse Amazon Mechanical Turk to create categorical labels for each set of possible responses.
• DUse Amazon Textract to create numeric features for each set of possible responses.

Explanation

To comprehensively represent multiple selections for each survey question in a dataset for logistic regression, one-hot encoding is the most appropriate method. This technique converts each possible option into a binary feature, clearly indicating chosen responses.

Common mistakes.

• B. Binning is used for categorizing continuous numerical data into ranges, not for representing discrete, multi-select categorical choices from a survey.
• C. Amazon Mechanical Turk is a crowdsourcing service for human-based tasks like data labeling, not for the automated structuring of existing survey responses into a dataset.
• D. Amazon Textract is an optical character recognition (OCR) service for extracting text from documents, which is irrelevant for processing digital survey responses.

Concept tested. Data preparation - one-hot encoding for multi-label categorical features

Reference. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html

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