AI Associate Data for AI Practice Question
A company is implementing Einstein Prediction Builder to predict whether a support case will escalate. Which TWO data preparation steps should the admin take to improve model accuracy?
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 missing values are handled appropriately (e.g., imputed or excluded)
Correct: Removing redundant fields (like record IDs) and handling missing values are crucial for model accuracy. Option A is wrong because more fields can introduce noise. Option C is wrong because data encryption is about security, not accuracy. Option D is wrong because all cases should be included to represent the full pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Include as many fields as possible to provide more context
Why it's wrong here
Including irrelevant fields can reduce model accuracy.
- ✓
Ensure missing values are handled appropriately (e.g., imputed or excluded)
Why this is correct
Missing values can bias the model; proper handling improves accuracy.
- ✗
Encrypt all fields containing personally identifiable information
Why it's wrong here
Encryption is a security measure, not a data preparation step for model accuracy.
- ✗
Exclude cases that were closed without escalation
Why it's wrong here
Excluding non-escalated cases would remove the negative examples needed for training.
- ✓
Remove fields that have a one-to-one relationship with the outcome
Why this is correct
Fields like Case Number or Created Date are unique and not predictive.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.