AWS Certified Machine Learning Specialty · Free Practice Question Medium
Question 3
The data science team at an analytics company is working on a credit score model using SageMaker Linear Learner algorithm. The training data consists of these fields : name, age, annual salary, gender, employment status and credit score. The model needs to predict the credit score label.
Which of the following data preparation steps need to be completed before working on the model?
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A
Drop the age and one-hot-encode name
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B
Drop the age and one-hot-encode name and credit score
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C
Drop the name and one-hot-encode gender and employment status
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D
Drop the name and one-hot-encode annual salary and credit score
Reveal correct answer
Correct answer: C
Explanation
Correct option:
Drop the name and one-hot-encode gender and employment status
One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction. A categorical variable can take on a limited, and usually fixed, number of possible values.
As gender and employment status are categorical they need to be one-hot-encoded. Name has no bearing as a useful feature for the model, so it can be discarded.
Incorrect options:
Drop the age and one-hot-encode name
Drop the age and one-hot-encode name and credit score
You cannot one-hot encode "name" as it's not a categorical variable, so both these options are incorrect.
Drop the name and one-hot-encode annual salary and credit score - You cannot one-hot encode annual salary as it's not a categorical variable.
Discussion
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