AI Associate AI Capabilities in CRM Practice Question
This AI Associate practice question tests your understanding of ai capabilities in crm. 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.
Which TWO statements are true about Einstein Prediction Builder in Salesforce? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It can use related object fields as predictors in the model.
Option D is correct because Einstein Prediction Builder can include fields from related objects (e.g., child objects or lookup objects) as predictors in the model, enabling richer data inputs for predictions. This is achieved through the platform's ability to traverse relationships and aggregate data from related records, which significantly enhances model accuracy.
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.
✗
It is only available for lead scoring models.
Why it's wrong here
Prediction Builder can predict outcomes on any object, not just leads.
✗
It only supports predictions on the Opportunity object.
Why it's wrong here
Prediction Builder supports multiple objects, including custom ones.
✗
The prediction model automatically retrains every 24 hours.
Why it's wrong here
Training frequency is adjustable, not fixed to 24 hours.
✓
It can use related object fields as predictors in the model.
Why this is correct
Related object fields can be included as input features.
Related concept
Read the scenario before looking for a memorised answer.
✓
It allows users to create custom predictions using fields from standard and custom objects.
Why this is correct
Users select object fields as predictors for custom predictions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Einstein Prediction Builder is limited to lead scoring or a single object, but Salesforce designed it to be object-agnostic, and the automatic retraining interval is not 24 hours but rather triggered by data changes or a configurable schedule.
Detailed technical explanation
How to think about this question
Under the hood, Einstein Prediction Builder uses automated machine learning (AutoML) to select the best algorithm (e.g., gradient boosting, logistic regression) based on the data. It automatically handles feature engineering by flattening related object fields into the training dataset, and it enforces a minimum of 10,000 records for model training to ensure statistical significance. A real-world scenario is predicting case closure time using fields from the Account object (e.g., industry) and child Cases (e.g., priority history).
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.
AI Capabilities in CRM — This question tests AI Capabilities in CRM — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: It can use related object fields as predictors in the model. — Option D is correct because Einstein Prediction Builder can include fields from related objects (e.g., child objects or lookup objects) as predictors in the model, enabling richer data inputs for predictions. This is achieved through the platform's ability to traverse relationships and aggregate data from related records, which significantly enhances model accuracy.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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