AI Associate Data for AI Practice Question
A retail company uses Einstein Next Best Action with customer data from Data Cloud. The recommendations are not personalized. The admin checks the data quality dashboard and finds that the 'Customer_Profile' object has 40% records with missing 'PreferredChannel' field. What is the best course of action?
⚠ Common exam trap
The trap here is that candidates might think removing the field or filtering out incomplete records is simpler, but Salesforce often tests the understanding that imputation is a standard, non-destructive method to handle missing data in AI models, especially when the missing rate is high.
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
✓
Impute the missing values using the mode of the field.
Imputing missing values using the mode (most frequent value) of the 'PreferredChannel' field is a standard data preprocessing technique that preserves the dataset size and statistical distribution. In Einstein Next Best Action, missing categorical data can degrade model personalization, and mode imputation is a simple, effective way to handle this without losing records or altering the model structure.
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 the field from the model.
Why it's wrong here
Removing potentially important fields reduces model accuracy.
- ✓
Impute the missing values using the mode of the field.
Why this is correct
Imputation is a standard data cleaning technique that maintains dataset size and field utility.
- ✗
Increase the data refresh frequency.
Why it's wrong here
Refreshing does not fix missing data.
- ✗
Train the model with only records that have non-null PreferredChannel.
Why it's wrong here
Reducing dataset size can harm model performance.
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