AI Associate Data for AI Practice Question
A marketing team wants to use Einstein Recommendations to personalize product offers on their e-commerce site. They have a dataset of 50,000 customers with purchase history. However, 40% of customers have no purchase history (new registrations). The model performs well for returning customers but gives generic recommendations for new ones. The team wants to improve recommendations for new customers. What data preparation step should they take?
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 missing purchase history with the average purchase frequency across all customers.
Imputing missing purchase data with a sensible default (e.g., average purchase frequency) gives the model signal for new customers, improving recommendations without discarding data.
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 customers with missing purchase history from the training set.
Why it's wrong here
Removing 40% of customers reduces training data and does not solve the cold-start issue for new customers.
- ✗
Assign a random purchase frequency to each new customer to add variety.
Why it's wrong here
Random values introduce noise and do not reflect realistic behavior, likely harming model performance.
- ✓
Impute missing purchase history with the average purchase frequency across all customers.
Why this is correct
Imputation provides a baseline signal for new customers, enabling the model to make reasonable recommendations.
- ✗
Use only customers with complete purchase history to train a more accurate model.
Why it's wrong here
This ignores the problem and provides no improvement for new customers.
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