AI Associate AI Fundamentals Practice Question
A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)
⚠ Common exam trap
Salesforce often tests the misconception that all input features must be numerical for machine learning models, but Einstein Prediction Builder natively supports non-numerical data types through automated preprocessing.
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
✓
Missing values in features should be handled appropriately.
Missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Missing values in features should be handled appropriately.
Why this is correct
Missing data can bias the model.
- ✓
The dataset should span a sufficient time period to capture patterns.
Why this is correct
Sufficient time captures trends.
- ✗
All input features must be numerical.
Why it's wrong here
Categorical features can be encoded.
- ✗
The model should be retrained only once after initial deployment.
Why it's wrong here
Periodic retraining is necessary.
- ✓
The prediction horizon must be clearly defined.
Why this is correct
Defines the target time frame.
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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.