The answer is to filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority. This directly addresses the core issue of data drift—the last three months contained an anomalous escalation pattern caused by a product bug that has since been fixed, so including that period would bias the model toward false positives. Imputing the missing priority values with the mode prevents the model from learning noise from 60% nulls while preserving the field’s predictive signal, which maintains recall. On the Salesforce AI Associate exam, this scenario tests your understanding of how retraining Einstein models to handle data drift requires both removing temporally skewed data and fixing data quality issues like missing values. A common trap is to simply retrain on all data or drop the problematic field entirely, which would harm recall. Memory tip: “Drift and gaps—trim the shift, fill the gaps.”
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Einstein Prediction Builder uses automated machine learning (AutoML) with gradient boosting or logistic regression as base learners. Missing values in categorical fields like 'Priority__c' are often treated as a separate category, which can introduce noise if the null rate is high (60%). Imputing with the mode reduces sparsity and helps the model learn consistent patterns. Data drift from the last 3 months creates a temporal bias that inflates recall but harms precision because the model overfits to a non-recurring event; filtering this period aligns training data with the current distribution.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority. — Option C is correct because 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A global manufacturing company uses Sales Cloud and has implemented Einstein Opportunity Scoring to prioritize deals. The scoring model was trained on historical data and initially performed well. Over the past month, the scores have become less accurate, with many high-scoring opportunities not closing and some low-scoring ones closing. The admin notices that the sales team has been using a new discounting strategy that heavily influences deal outcomes. The admin wants to improve model performance without manual intervention. Which action should the admin take?
hard
A.Manually adjust the field weights for discount-related fields in the model.
✓ B.Retrain the Einstein Opportunity Scoring model with the latest opportunity data including discount information.
C.Run a data quality report to identify and clean missing discount data.
D.Create a custom field for discount percentage and add it to the model.
Why B: Option B is correct because retraining the Einstein Opportunity Scoring model with the latest opportunity data, including discount information, allows the machine learning model to automatically learn the new patterns introduced by the sales team's discounting strategy. This aligns with the AI Associate principle that models must be retrained on current data to maintain accuracy when business processes change, without requiring manual intervention.
Last reviewed: Jun 30, 2026
Question Discussion
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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.
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