AI Associate Practice Test 18 — 15 Questions

Question 1

A service manager wants to automatically categorize incoming cases into standard fields like Type, Priority, and Reason based on historical case data. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification uses AI to automatically classify cases into fields such as Type, Priority, and Reason by learning from historical case data.

Answer

Einstein Discovery

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Einstein Prediction Builder

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Einstein Article Recommendations

Question 2

A sales team wants to use Einstein Opportunity Scoring to improve win rates. Which TWO statements about Einstein Opportunity Scoring are correct? (Choose 2)

Accepted Answer

It provides a score from 1 to 99 indicating the likelihood of winning the opportunity. Opportunity Scoring predicts win likelihood (1-99) and factors are visible in Lightning. Historical data is used, but the score is not directly accessible via external API without additional setup.

Answer

It requires a minimum of 500 closed opportunities in the last 12 months to activate

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The score can be accessed via the REST API out-of-the-box

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It automatically updates the opportunity stage based on the score

Question 3

A company wants to build an autonomous AI agent that can take actions in Salesforce, such as updating records and sending emails, based on user instructions. Which tool should they use?

Accepted Answer

Agentforce Agent Builder. Agentforce with Agent Builder allows creation of autonomous agents that can perform actions in Salesforce.

Answer

Einstein Copilot

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Einstein Bots

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Flow Builder

Question 4

An admin needs to generate personalized sales email drafts for their team using generative AI. The emails should be based on context from the Salesforce record. Which feature should they use?

Accepted Answer

Sales GPT. Sales GPT is the Einstein GPT feature that generates email drafts for sales reps based on record context.

Answer

Prompt Builder

Answer

Einstein Copilot

Answer

Einstein Next Best Action

Question 5

A sales operations manager wants to use Einstein Lead Scoring to prioritize leads. Where can the lead score be viewed in Salesforce?

Accepted Answer

As a field on the lead record and in list views. Einstein Lead Scoring surfaces the lead score as a field on the lead object, making it available in list views, reports, and the record page.

Answer

Only in Einstein Analytics dashboards

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Only in the Einstein Lead Scoring setup page

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In the Einstein Lead Scoring mobile app only

Question 6

A company wants to create a custom AI model that predicts whether a support case will be escalated (Yes/No) based on historical case data. They need to define the objective field, training data, and features. Which Einstein feature allows them to build this custom prediction?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder enables creation of custom binary classification models using Salesforce data, allowing selection of prediction field, data set, and features.

Answer

Einstein Case Classification

Answer

Einstein Next Best Action

Answer

Einstein Discovery

Question 7

A support center wants to use Einstein Case Classification to automatically assign categories to incoming cases. They have historical case data with the 'Type' field populated for 70% of cases, 'Priority' for 50%, and 'Reason' for 30%. They want to classify on 'Type' and 'Reason'. What is the best approach to maximize model accuracy?

Accepted Answer

Start by training a model for Type only, since it has more populated records, then train a model for Reason once more data is accumulated.. Einstein Case Classification requires a minimum of 1500 records with the target field populated. Prioritizing the field with more populated records (Type) ensures a larger training set, improving accuracy. Reason can be added later once more data is available.

Answer

Use Einstein Prediction Builder instead, which can handle multi-class classification for both fields.

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Build a single model that predicts both Type and Reason simultaneously to leverage all available data.

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Create two separate models: one for Type using all records with Type populated, and one for Reason using all records with Reason populated.

Question 8

A company uses Einstein Conversation Insights to analyze sales calls. They want to identify when a competitor is mentioned and automatically log that mention to the opportunity. Which feature should they configure?

Accepted Answer

Keyword Tracking in Conversation Insights. Einstein Conversation Insights allows keyword tracking, where you can define keywords (e.g., competitor names) and set actions like logging to a record.

Answer

Einstein Next Best Action

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Einstein Email Insights

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Einstein Activity Capture

Question 9

A company wants to build a custom AI prediction for churn rate using their Customer_Churn__c (Yes/No) field. In Einstein Prediction Builder, after selecting the object and prediction field, what is the next step?

Accepted Answer

Select the dataset (records to train on).. After selecting the prediction field (binary classification), you must select the dataset (records to train on) and then choose features (input fields) that the model will use to make predictions.

Answer

Define the prediction score field and explanation field.

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Deploy the model to production.

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Select features (input fields) to train the model.

Question 10

A healthcare organization needs to automatically classify incoming cases into predefined categories (e.g., billing, clinical, technical) based on the case description. They have historical case data with known categories. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Case Classification. Einstein Case Classification uses AI to automatically assign field values like Type, Priority, and Reason on cases based on historical data.

Answer

Einstein Vision and Language Platform

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Question 11

Which Einstein feature provides AI-powered predictions for opportunity win likelihood?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is a dedicated feature that predicts opportunity win probability on a scale of 1-99.

Answer

Einstein Forecasting

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Einstein Lead Scoring

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Einstein Prediction Builder

Question 12

A sales operations manager notices that Einstein Lead Scoring is not producing scores for some leads. The leads have all required fields populated. What is the most likely cause?

Accepted Answer

There are fewer than 500 leads with the score field populated. Einstein Lead Scoring requires a minimum number of leads (usually 500) with the score field populated before it can start generating scores. Fewer leads mean the model cannot be built.

Answer

The user does not have the 'View Lead Score' permission

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The lead source field is not included as a feature

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The leads were created in a different Salesforce instance

Question 13

A company uses Einstein Lead Scoring and finds that leads with high scores are not converting. What should the admin do to improve prediction accuracy?

Accepted Answer

Retrain the model with more recent conversion data. Retraining the model with more recent conversion data allows the Einstein Lead Scoring model to adapt to changing patterns in lead behavior and conversion criteria. When high-scoring leads fail to convert, it indicates that the historical data used to train the model no longer reflects current conversion dynamics, so refreshing the training dataset improves prediction accuracy by aligning the model with recent outcomes.

Answer

Increase the scoring model's maximum score

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Disable field-level security for scoring fields

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Lower the lead conversion threshold

Question 14

Which TWO considerations are critical when planning data labeling for a computer vision project in a regulated industry?

Accepted Answer

Mitigating labeler bias to ensure fairness. Labeler bias can introduce systematic errors into the training data, leading to models that perform unfairly or inaccurately across different demographic groups. In regulated industries, such bias can violate anti-discrimination laws and regulatory standards, making its mitigation a critical planning consideration.

Answer

Data storage location for label files

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Labeling timeline and budget constraints

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Choosing between bounding boxes and segmentation masks

Question 15

An admin sets up Einstein Sentiment scoring for case comments. After a week, they notice that most comments are scored as 'Neutral' even when customer sentiment is clearly negative. What should the admin check first?

Accepted Answer

Retrain the sentiment model with industry-specific training data. Einstein Sentiment is a pre-trained model that may not accurately interpret sentiment in industry-specific contexts. Retraining the model with domain-specific training data (e.g., using Salesforce's Intent and Sentiment API with custom datasets) adjusts the model to recognize sentiment nuances in that industry, improving accuracy beyond the generic baseline.

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Increase the model confidence threshold to reduce false positives

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Switch to Einstein Bot for sentiment analysis

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Verify that case comments contain at least 50 words each