AI Associate AI Fundamentals Practice Question
Which TWO actions are best practices when implementing Einstein Prediction Service?
⚠ Common exam trap
Salesforce often tests the misconception that more data always leads to better predictions, but in practice, irrelevant or noisy features degrade model performance, making feature selection and data cleaning critical.
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 data to handle missing values and outliers.
Data quality directly impacts the accuracy and reliability of Einstein Prediction Service models. Cleaning data to handle missing values and outliers ensures that the training data is representative and reduces the risk of skewed predictions, which is a fundamental prerequisite for any machine learning model within Salesforce's AI framework.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore correlated features to simplify the model.
Why it's wrong here
Correlated features can be handled but not ignored.
- ✓
Clean the data to handle missing values and outliers.
Why this is correct
Data cleaning improves model accuracy.
- ✓
Select relevant features that are likely to influence the prediction.
Why this is correct
Feature selection reduces overfitting and improves performance.
- ✗
Include all available fields in the dataset for maximum information.
Why it's wrong here
Including all fields can introduce noise and overfitting.
- ✗
Use the default field mapping without review.
Why it's wrong here
Reviewing mapping ensures correct data usage.
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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.