AI Associate AI Fundamentals Practice Question
Network Topology
Refer to the exhibit. An admin runs a preprocess script before training an Einstein model. Why is normalization applied to the 'AnnualRevenue' and 'NumberOfEmployees' columns?
⚠ Common exam trap
Salesforce often tests the distinction between data preprocessing steps (normalization, scaling) and other data preparation tasks (outlier detection, missing value handling, dimensionality reduction), and the trap here is that candidates confuse normalization with outlier detection or feature reduction because both involve numerical transformations.
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
✓
To ensure both features contribute equally to the model
Normalization scales features like 'AnnualRevenue' and 'NumberOfEmployees' to a comparable range (e.g., 0–1 or with zero mean and unit variance). Without normalization, a feature with larger numeric values (e.g., revenue in millions) would dominate distance-based calculations in models like k-nearest neighbors or gradient descent, causing the model to undervalue the smaller-scale feature. By normalizing, both features contribute equally to the model's learning process, which is essential for many machine learning algorithms used in Einstein.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To detect outliers in the data
Why it's wrong here
Outlier detection is not the purpose of normalization; it may even mask outliers.
- ✓
To ensure both features contribute equally to the model
Why this is correct
Equalizing scales prevents one feature from having undue influence.
- ✗
To remove rows with missing values
Why it's wrong here
Missing values are handled by the --drop-missing flag, not normalization.
- ✗
To reduce the number of features from 30 to 2
Why it's wrong here
Normalization does not reduce feature count; it only transforms values.
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