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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 9 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 9
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

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An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

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Suggested Answer:
by abrarjahin at Jan. 28, 2025, 4:52 p.m.
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molerowan
1 day, 17 hours ago
Size of building (Square feet or Square Meters) = Logarithmic transformation Explanation: Building size is a numerical feature that often shows a skewed distribution and can have a non-linear relationship with price. Logarithmic transformation is suitable because: It helps normalize skewed distributions It can help linearize the relationship between size and price It's particularly useful for features that follow exponential or multiplicative patterns Real estate data often shows log-normal distributions
upvoted 1 times
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molerowan
1 day, 17 hours ago
Type_year (type of home and year it was built) = Feature splitting Explanation: This feature contains two different pieces of information (type and year) combined in one column. Feature splitting is appropriate because: It separates the compound feature into its components The type can then be one-hot encoded The year can be treated as a numerical feature This separation allows the model to learn from each component independently
upvoted 1 times
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molerowan
1 day, 17 hours ago
City (Name) = One-hot encoding Explanation: City names are categorical variables that don't have any numerical relationship with each other. One-hot encoding is the best technique for this type of data because: It creates binary columns for each unique city It avoids introducing artificial ordering between cities It allows the model to treat each city as an independent feature
upvoted 1 times
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djeong95
2 days, 14 hours ago
city = one-hot encoding; type_year = feature splitting; size of the building = standardized distribution
upvoted 1 times
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abrarjahin
1 week, 3 days ago
Size of the building is standard distribution
upvoted 2 times
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