AI Associate Data for AI Practice Question
A company plans to use Einstein Discovery to analyze sales data. Which data preparation step is essential for time-series forecasting?
⚠ Common exam trap
Salesforce often tests the misconception that data normalization (scaling) is always required for AI models, but for tree-based algorithms like those in Einstein Discovery, scaling is irrelevant, and the trap is that candidates pick Option D thinking it is a universal preprocessing step.
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
✓
Ensure date fields are properly formatted and contain sufficient historical range
For time-series forecasting in Einstein Discovery, the date field must be properly formatted (e.g., as a date or datetime data type) and contain a sufficient historical range to identify patterns like seasonality and trends. Without adequate historical data, the model cannot learn temporal dependencies, making this step essential.
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 outliers in sales amounts
Why it's wrong here
Outliers may be important signals like promotions.
- ✓
Ensure date fields are properly formatted and contain sufficient historical range
Why this is correct
Einstein Discovery relies on date fields for trend detection.
- ✗
Remove duplicate records
Why it's wrong here
Duplicates can cause bias but are not time-series specific.
- ✗
Scale all numeric fields to a 0-1 range
Why it's wrong here
Scaling helps but is not the most essential for time series.
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This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.