You are a machine learning expert working for a marketing firm. You are supporting a team of data scientists and marketing managers who are running a marketing campaign. Your data scientists and marketing managers need to answer the question, “Will this user subscribe to my campaign?” You have been given a dataset in the form of a CSV file which is formatted as such:
UserId, jobId, jobDescription, educationLevel, campaign, duration, willRespondToCampaign
When you perform feature engineering on this dataset, which of the following data types would you use to define the willRespondToCampaign attribute?

Answer options:

A.CATEGORICAL
B.TEXT
C.BINARY
D.Numeric

Answer: C
Option A is incorrect because you choose the CATEGORICAL data type for an attribute that holds a limited set of unique strings. For example, a user name, the region, and a product code are categorical values. The willRespondToCampaign attribute takes on either ‘yes’ or ‘no’ values, which are binary in nature.
Option B is incorrect because for each user observation you are trying to discern “Will this user subscribe to my campaign?” You are solving for a "yes" or "no" answer, which is binary data type, not a text data type.
Option C is correct because you choose the BINARY data type for an attribute that only has two possible values, such as yes or no, or true or false. The attribute willRespondToCampaign has only two possible answers: yes or no.
Option D is incorrect because the willRespondToCampaign feature holds a "yes" or "no" value, you should define it as a binary data type, not a numeric data type.
Reference:
Please see the Machine Learning Mastery article titled Discover Feature Engineering, How to Engineer Features and How to Get Good at It
https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
for a complete description of the schema attributes.