Answer-first summary for fast verification
Answer: Yes
The correct answer is A (Yes) because the Custom substitution value option in Azure ML's Clean Missing Data module allows specifying a placeholder value (like 0 or NA) to replace all missing values, provided it's compatible with the column's data type. This directly addresses the requirement to 'detect and fix' null and missing values. Community discussion supports this with 79% choosing A, citing Microsoft documentation that confirms this functionality. While some comments (e.g., advocating for row removal or imputation) suggest alternatives, they don't invalidate the Custom substitution value as a valid method; the question specifically asks if this recommendation satisfies the requirements, and it does.
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You are creating a machine learning model using a dataset that contains rows with null and missing values. You plan to use the Clean Missing Data module in Azure Machine Learning Studio to detect and fix these values.
Recommendation: Use the Custom substitution value option.
Does this recommendation satisfy the requirements?
A
Yes
B
No
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