- A
Roll back the model to the version trained 6 months ago when escalation rates were lower.
Why wrong: Old model may have different biases and not reflect current behavior.
- B
Exclude the 'Priority__c' field from the model and retrain.
Why wrong: Exclusion removes a potentially strong predictor; better to handle nulls.
- C
Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.
Removing anomalous period and fixing data quality improves model relevance.
- D
Remove the 'Is_Critical__c' field and increase training data to 50,000 records.
Why wrong: Removing it may help, but data increase may include more stale data.
Question 425 of 1,000
hardMultiple ChoiceObjective-mapped
AI Associate Practice Question: A mid-sized company uses Salesforce for sales and…
This AI Associate practice question tests your understanding of ai associate exam topics. 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 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.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
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FAQ
Questions learners often ask
What does this AI Associate question test?
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.
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Last reviewed: Jun 30, 2026
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