AI Associate Data for AI Practice Question
Which THREE of the following are best practices for feature engineering in Einstein Studio?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply normalization to numerical features
Options B, D, and E are correct. Normalization scales features, domain knowledge creates meaningful derived features, and one-hot encoding handles categorical variables. Option A is wrong because raw data often needs processing. Option C is wrong because removing missing values can lose information; imputation is often better.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all records with missing values
Why it's wrong here
May discard valuable data; consider imputation instead.
- ✓
Apply normalization to numerical features
Why this is correct
Normalization ensures features are on a similar scale.
- ✗
Use raw data directly without any transformation
Why it's wrong here
Raw data may not be suitable for models due to scale and encoding.
- ✓
Use domain knowledge to create derived features
Why this is correct
Derived features often capture important patterns.
- ✓
Use one-hot encoding for categorical variables
Why this is correct
Standard technique to represent categories numerically.
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