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AI Associate Data for AI Practice Question

Which TWO data preparation steps are required before using Einstein Discovery for sales forecasting? (Choose 2)

⚠ Common exam trap

Salesforce often tests the misconception that manual data preprocessing steps like normalization or one-hot encoding are required, when in fact Einstein Discovery automates these steps, and the key prerequisite is ensuring a proper date/timestamp field exists for time-based analysis.

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

Include a date or timestamp field for time series analysis

Einstein Discovery requires a date or timestamp field to perform time series analysis, which is essential for identifying trends, seasonality, and patterns in historical sales data. Without this field, the model cannot properly order observations or forecast future values based on temporal dependencies.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Convert all text fields to numeric using one-hot encoding

    Why it's wrong here

    Einstein Discovery can handle categorical data; manual encoding is not required.

  • Remove duplicate records

    Why it's wrong here

    While good practice, duplicates are not automatically handled but can cause bias; not a strict requirement.

  • Include a date or timestamp field for time series analysis

    Why this is correct

    For forecasting, a date field is needed to order records.

  • Ensure all predictor fields have no missing values

    Why this is correct

    Missing values can cause errors during model training.

  • Normalize numeric fields to a 0-1 scale

    Why it's wrong here

    Normalization is handled internally by the algorithm.

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Last reviewed: Jun 30, 2026

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