AI Associate Practice Test 45 — 15 Questions

Question 1

A company wants to use Einstein Prediction Builder to predict whether a support case will escalate (binary: escalate or not). They have historical case data in Salesforce. Which THREE steps are required to set up this prediction?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Select the dataset (records to train on). Selecting the dataset is a fundamental step in setting up an Einstein Prediction Builder model. You must specify which historical case records to use for training, ensuring the data includes both escalated and non-escalated cases so the model can learn patterns. Without a properly defined dataset, the prediction cannot be built.

Answer

Build an Einstein Bot for escalation handling

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Create a dashboard to monitor model performance

Question 2

A developer is building an autonomous AI agent with Agentforce. They need the agent to perform actions in Salesforce, such as updating records and sending emails. How should they define these capabilities in Agent Builder?

Accepted Answer

Create actions in Agent Builder, specifying the operation and parameters. In Agent Builder, actions are the mechanism that defines what an autonomous AI agent can do in Salesforce, such as updating records or sending emails. Each action specifies the operation (e.g., a standard or custom action) and its parameters, allowing the agent to execute precise tasks without additional coding.

Answer

Define topics that correspond to each action

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Use Prompt Builder to create prompts for each action

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Write Apex triggers to handle agent requests

Question 3

A data scientist is evaluating Salesforce's Einstein features for predictive analytics. Which three statements accurately describe Einstein Discovery? (Select three answers.)

Accepted Answer

It can generate natural language explanations of model insights.. Einstein Discovery includes a natural language generation (NLG) engine that automatically produces plain-English explanations of model insights, such as key drivers and predictions. This allows non-technical users to understand the output without needing to interpret raw statistical data.

Answer

It is limited to 1000 rows of data per prediction.

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It requires a separate data preparation tool before use.

Question 4

A sales rep wants to quickly generate a personalized email to a lead based on their CRM record and recent activity. Which Einstein GPT feature enables this?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it is specifically designed for sales use cases, enabling sales reps to generate personalized emails based on CRM data and recent activity. It leverages generative AI to draft contextual content directly within Salesforce, streamlining outreach without requiring manual composition.

Answer

Einstein Copilot

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Service GPT

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Einstein Discovery

Question 5

A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?

Accepted Answer

The model is unfair to Spanish-speaking customers.. The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

Answer

The model is not interpretable.

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The model has low accuracy for Spanish reviews.

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The model violates privacy regulations.

Question 6

A healthcare organization is deploying an AI model to predict patient readmission risk. The model was trained on historical data that underrepresented minority populations. During testing, the model shows lower accuracy for those groups. What should the data scientist do first?

Accepted Answer

Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques.. The first step in addressing model bias is to audit the training data for representational imbalance. Re-evaluating the data and applying re-sampling techniques (e.g., oversampling minority groups or undersampling the majority) directly targets the root cause of the disparity—skewed class distributions—before modifying the model or its features.

Answer

Remove sensitive attributes like race and gender from the training data.

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Ignore the disparity because the model's overall accuracy is acceptable.

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Retrain the model with more complex algorithms to improve accuracy.

Question 7

A company wants to use AI to automatically recommend the best next action for service agents during a live chat with a customer. Which Einstein feature should be enabled?

Accepted Answer

Einstein Next Best Action. Einstein Next Best Action, is correct because it is specifically designed to recommend the optimal next step for service agents during live interactions. It uses AI to analyze the customer context, conversation history, and business rules to surface the most relevant action, such as offering a discount or escalating the case, directly within the agent's console.

Answer

Einstein Article Recommendations

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Einstein Bots

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Einstein Reply Recommendations

Question 8

A sales operations manager wants to automatically log all emails and events from sales reps' Outlook accounts to Salesforce without manual setup. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct feature because it automatically syncs emails and events from Microsoft 365 or Google Workspace into Salesforce without requiring manual setup or user-installed add-ins. It uses a server-side integration that logs activities directly to Salesforce records, meeting the requirement for automatic logging of Outlook emails and events.

Answer

Einstein Conversation Insights

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Einstein Lead Scoring

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Einstein Email Insights

Question 9

A company wants to use Einstein GPT to generate draft replies for service agents. Which TWO Einstein GPT features can accomplish this?

Accepted Answer

Service GPT – Reply Recommendations. Service GPT includes reply recommendations and case summary generation. Sales GPT is for sales. Einstein Copilot can assist but is not specifically for reply drafts. Einstein Bots are for chat automation.

Answer

Einstein Copilot

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Sales GPT – Email Generation

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Einstein Bots

Question 10

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

Accepted Answer

The model training data size and distribution of values.. Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.

Answer

The number of fields used as input features.

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The API version used in the integration.

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The permission set for the Einstein AI feature.

Question 11

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Salesforce Einstein feature should be used?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to automatically prioritize leads based on their likelihood to convert, using historical data and machine learning models. It assigns a score (0–100) to each lead, enabling sales teams to focus on high-conversion leads without manual intervention.

Answer

Einstein Opportunity Scoring

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Einstein Prediction Builder

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Einstein Discovery

Question 12

A service manager wants to automatically categorize incoming support cases into appropriate Type, Priority, and Reason fields based on the case description. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate case fields such as Type, Priority, and Reason based on the case description. It uses natural language processing (NLP) to analyze the text and map it to predefined picklist values, enabling automated categorization without manual rules.

Answer

Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action

Question 13

A service manager wants to reduce case resolution time by automatically categorizing incoming cases and suggesting relevant knowledge articles. The team has limited data science expertise. Which combination of Einstein features should be used?

Accepted Answer

Einstein Case Classification and Einstein Article Recommendations. Einstein Case Classification uses machine learning to automatically categorize incoming cases based on historical data, and Einstein Article Recommendations suggests relevant knowledge articles to agents, directly addressing the goal of reducing case resolution time without requiring extensive data science expertise.

Answer

Einstein Lead Scoring and Einstein Discovery

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Einstein Bots and Einstein Conversation Insights

Answer

Einstein Vision and Language Platform

Question 14

Refer to the exhibit. A bot developer sees this error during Einstein Bot deployment. What is the correct action to resolve the issue?

Accepted Answer

Add at least 5 training phrases to the 'OrderStatus' intent. The error indicates that the 'OrderStatus' intent has fewer than the required minimum number of training phrases. Einstein Bot requires at least 5 training phrases per intent to ensure reliable natural language processing (NLP) model training. Adding at least 5 training phrases to the 'OrderStatus' intent satisfies this requirement and resolves the deployment error.

Answer

Reduce the minimum required phrases in bot settings

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Remove the 'OrderStatus' intent from the bot

Answer

Increase the confidence threshold for all intents

Question 15

An admin notices that Einstein Opportunity Scoring is not generating scores for new opportunities created in the past week. Which troubleshooting step should the admin take first?

Accepted Answer

Check that there are at least 50 won and 50 lost opportunities with populated fields. Einstein Opportunity Scoring requires a minimum of 50 won and 50 lost opportunities with populated fields to generate scores. Without this historical data, the model cannot learn patterns to score new opportunities. The admin should first verify this prerequisite before considering other steps.

Answer

Retrain the Opportunity Scoring model

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Verify that users have the 'View Einstein Scores' permission

Answer

Wait 48 hours for the model to update