AI Associate Data for AI Practice Question
A large retail company uses Data Cloud to consolidate customer data from e-commerce, POS, and loyalty programs. They plan to use Einstein Studio to build a churn prediction model. The data architect notices that the churn model's accuracy is below expectations. Upon investigation, they find that the customer entity in Data Cloud has multiple records for the same customer with slightly different spellings and addresses. The data comes from different streams. What should the data architect do to improve the model?
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
✓
Create a Data Transform to merge duplicate records based on fuzzy matching on name and address fields
The best course of action because creating a Data Transform with fuzzy matching merges duplicates into a single clean record, improving data quality for the model. Option B is flawed because increasing frequency does not fix existing duplicates. Option C aggregates but doesn't resolve the duplication. Option D changes the primary key but duplicates remain.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a Data Transform to merge duplicate records based on fuzzy matching on name and address fields
Why this is correct
Directly addresses the duplicate issue and creates a unified view.
- ✗
Increase the data stream frequency to get more recent data
Why it's wrong here
Does not fix existing duplicates; might add more.
- ✗
Change the primary key in the data model to use a different identifier
Why it's wrong here
Does not resolve the underlying duplicate records.
- ✗
Use a Calculated Insight to aggregate customer behavior over time
Why it's wrong here
Aggregation doesn't remove duplicates from the entity.
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