AI Associate AI Fundamentals Practice Question
A mid-sized company uses Salesforce for sales and service. They have implemented Einstein Prediction Builder on a custom object 'Support_Ticket__c' to predict whether a ticket will be escalated (field: 'Escalated__c' Boolean). The model was trained with 10,000 records and 15 fields including 'Subject', 'Description_Summary__c', 'Priority__c', 'Hours_to_Resolution__c', and others. After deployment, the model's precision for escalated tickets is only 30%, while recall is 80%. The business finds too many false positives. The admin notices that the 'Priority__c' field has many missing values (60% null) and that the field 'Is_Critical__c' (a formula field) was included though it flags tickets as critical only rarely. The data spans 12 months but the last 3 months have a significantly higher escalation rate due to a product bug that has since been fixed. Which course of action will most likely improve the model's precision without harming recall?
⚠ Common exam trap
Salesforce often tests the misconception that simply removing a problematic field or adding more data will fix model performance, when the real issue is data drift and missing value handling that require both temporal filtering and imputation.
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
✓
Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.
It addresses both the data drift and data quality issues that degrade precision. Excluding the last 3 months removes the biased escalation pattern caused by a fixed product bug, ensuring the model learns from stable historical patterns. Imputing missing 'Priority__c' values with the most common priority reduces noise from nulls without discarding the field entirely, which helps maintain recall by preserving predictive signal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Roll back the model to the version trained 6 months ago when escalation rates were lower.
Why it's wrong here
Old model may have different biases and not reflect current behavior.
- ✗
Exclude the 'Priority__c' field from the model and retrain.
Why it's wrong here
Exclusion removes a potentially strong predictor; better to handle nulls.
- ✓
Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.
Why this is correct
Removing anomalous period and fixing data quality improves model relevance.
- ✗
Remove the 'Is_Critical__c' field and increase training data to 50,000 records.
Why it's wrong here
Removing it may help, but data increase may include more stale data.
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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.