DatabricksCertified Machine Learning Associate
Domain 2Objective 7
Use One-Hot Encoding for Categorical Features MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 5)
Part of the Section 2: Data Processing domain, which makes up ~27% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~9–16 in this domain), expect 1–2 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)
23questions here
5free pages
5concepts
Questions 21–23
- 21
What is a potential downside of one-hot encoding compared to label encoding for a categorical column with many unique values?
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What is the required input type for Spark ML's OneHotEncoder?
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A data team is building a Spark ML pipeline to predict loan default. They have a categorical column 'employment_type' with values 'full_time', 'part_time', 'self_employed', 'unemployed'. They want to one-hot encode this column and then train a random forest classifier. Which pipeline stage order is correct?
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