AI Associate AI Fundamentals Practice Question
A retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?
⚠ Common exam trap
Salesforce often tests the misconception that feature selection or algorithm changes are the primary levers for accuracy, when in reality data cleaning is the foundational step that directly impacts model reliability in Einstein Prediction Service.
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
✓
Clean the dataset by handling missing values and outliers.
Handling missing values and outliers is the most critical data preparation step for Einstein Prediction Service because the underlying gradient boosting models (like XGBoost) are sensitive to data quality issues. Missing values can introduce bias or cause the model to misinterpret patterns, while outliers can disproportionately influence split decisions, reducing predictive accuracy for churn scenarios.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Select only the top three features based on correlation.
Why it's wrong here
Feature selection is secondary to data quality.
- ✓
Clean the dataset by handling missing values and outliers.
Why this is correct
Proper data cleaning ensures the model learns accurate patterns.
- ✗
Use a different algorithm like neural networks.
Why it's wrong here
Algorithm choice is less impactful than data quality.
- ✗
Increase the dataset size by collecting more customer records.
Why it's wrong here
More data without quality can degrade performance.
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