AI Associate Practice Questions 1 — 50 Questions

Question 1

A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what should the admin do?

Accepted Answer

Review training data for representativeness. Reviewing training data for representativeness helps identify and mitigate bias sources.

Answer

Use only predefined responses

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Disable the bot for sensitive topics

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Monitor conversations regularly

Question 2

A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?

Accepted Answer

Lead interaction history with emails and web activity.. Einstein Engagement Scoring analyzes lead interactions (email opens, clicks, web visits) to calculate engagement scores. Option A is correct. Option B is wrong because demographic data is not the primary input. Option C is wrong because historical conversion data is used for predictive scoring, not engagement. Option D is wrong because social media data is not a direct input.

Answer

Historical conversion data from closed opportunities.

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Lead demographic information like industry and company size.

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Social media posts and mentions of the company.

Question 3

A Salesforce admin needs to create a prompt template that generates a follow-up email after a meeting. Which Prompt Builder template type should be used?

Accepted Answer

Sales Email. The Sales Email template type in Prompt Builder is specifically designed for generating sales-related communications, such as follow-up emails after meetings. It includes pre-built fields and context (e.g., meeting notes, contact details) optimized for sales workflows, making it the correct choice for this use case.

Answer

Service Reply

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Field Generation

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Flex Prompt

Question 4

A marketing manager wants to prioritize leads with the highest likelihood of conversion. Which Einstein feature should they use?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring predicts conversion probability for each lead. Custom formulas are manual, approval processes and data export are not predictive.

Answer

Approval Processes

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Custom Formula Fields

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Data Export

Question 5

A sales rep wants to generate personalized email drafts for leads using AI. Which feature should the admin enable?

Accepted Answer

Einstein GPT. Einstein GPT is the correct feature because it is Salesforce's native generative AI tool that can automatically create personalized email drafts for leads by leveraging CRM data and natural language processing. Unlike other options, Einstein GPT is specifically designed for AI-driven content generation within the Salesforce ecosystem.

Answer

Workflow Rules

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Process Builder

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Email Templates

Question 6

A marketing team wants to display personalized product recommendations to website visitors in Experience Cloud. Which feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it is specifically designed to deliver personalized product recommendations to website visitors in Experience Cloud. It uses AI to analyze visitor behavior, purchase history, and product attributes to surface relevant items, directly matching the use case of displaying personalized product recommendations.

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Einstein Prediction Builder

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Einstein Next Best Action

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Einstein GPT

Question 7

A sales team is using Einstein Lead Scoring and notices that leads from a certain geographic region are consistently scored lower, even when the lead's profile matches high-performing customers from other regions. What is the most likely cause and recommended first step?

Accepted Answer

The model may be biased due to underrepresented data from that region in the training set. Audit the model for bias and review training data demographics.. The low scores for a specific region likely indicate bias in the model due to historical data imbalances. The first step should be to audit the model for bias using tools like Fairness in AI or reviewing training data distribution.

Answer

The model is accurate; the region is genuinely underperforming. The team should accept the scores.

Answer

The region has lower quality leads, so the scores are correct. The team should focus on other regions.

Answer

Retrain the model with more data from that region, even if it means duplicating records.

Question 8

An admin is setting up Einstein Bot for a customer service chat. The bot needs to collect the customer's account number before transferring to a human agent. What should the admin configure?

Accepted Answer

Create a Dialog to ask for the account number. A Dialog is the component that handles a conversation flow, including collecting information. Option A is incorrect because a Variable stores data but does not collect it. Option C is incorrect because a Rule determines logic, not collection. Option D is incorrect because a Set Value action sets a variable, but the overall collection happens within a Dialog.

Answer

Add a Variable with the account number field

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Define a Rule to validate the account number

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Use a Set Value action to assign the account number

Question 9

To identify common customer issues from chat transcripts using AI, which feature should be used?

Accepted Answer

Einstein Conversation Mining. Einstein Conversation Mining analyzes unstructured chat data to surface common themes. Surveys collect feedback, reports and dashboards show aggregated data but not insights from text.

Answer

Post-Interaction Survey

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Reports

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Dashboards

Question 10

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 dedicated Salesforce Einstein feature designed specifically to automatically prioritize leads based on their likelihood to convert. It uses predictive models that analyze historical lead data and engagement patterns to assign a score between 1 and 99, enabling sales teams to focus on high-conversion leads without manual effort.

Answer

Einstein Opportunity Scoring

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Einstein Prediction Builder

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Einstein Activity Capture

Question 11

A sales manager wants to automatically track emails and events from their sales team's Gmail accounts into Salesforce without manual logging. Which Salesforce feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (B) is the correct feature because it automatically syncs emails and events from Gmail (and Microsoft 365) into Salesforce without requiring manual logging by users. It uses a background synchronization process that captures activities based on configured rules, eliminating the need for manual entry or third-party integrations.

Answer

Einstein Lead Scoring

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Einstein Email Insights

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Einstein Conversation Insights

Question 12

A manufacturing company uses Salesforce Sales Cloud with Einstein Activity Capture to log emails and events automatically. The sales team has noticed that some emails from Microsoft Outlook are not appearing in the Einstein Activity Capture dashboard, even though the users have configured the connection correctly. The administrator checks the activity capture settings and sees that the data source is enabled and users have the correct permissions. The missing emails are from a specific domain, @supplier.com, which is a known vendor. What should the administrator do to ensure these emails are captured?

Accepted Answer

Add the @supplier.com domain to the allowlist in Einstein Activity Capture settings.. Einstein Activity Capture uses allowlists and blocklists to control which emails are captured. By default, emails from external domains may be excluded to reduce noise. Adding @supplier.com to the allowlist ensures that emails from that domain are explicitly included in the capture process, overriding any default filtering.

Answer

Increase the sync frequency for Einstein Activity Capture to every 5 minutes.

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Instruct users to manually log the supplier emails as activities in Salesforce.

Answer

Assign the 'Einstein Activity Capture User' permission set to the supplier's email domain.

Question 13

A company is building a text classification model for customer support tickets. They have a dataset of 10,000 tickets. The team decides to use active learning for labeling. Which approach best aligns with active learning principles?

Accepted Answer

Train a preliminary model and prioritize labeling tickets with low prediction confidence.. Active learning iteratively selects the most informative unlabeled data points for labeling, typically those with low prediction confidence from a preliminary model. This minimizes labeling effort while maximizing model performance, which is the core principle of active learning.

Answer

Randomly select 2,000 tickets and label them manually.

Answer

Use a pre-trained model to label all tickets automatically.

Answer

Have subject matter experts label all 10,000 tickets.

Question 14

A company has international customers and wants Einstein Prediction Builder to forecast deal closure probability. The data includes fields like 'region', 'product line', and 'deal amount'. What is a best practice to ensure the model works for all regions?

Accepted Answer

Group regions into broader categories like 'Americas', 'EMEA', 'APAC'.. Grouping regions into broader categories like 'Americas', 'EMEA', and 'APAC' reduces high cardinality and sparsity in categorical features, which improves model stability and prevents overfitting in Einstein Prediction Builder. This approach ensures each region group has sufficient training data to learn meaningful patterns, enabling the model to generalize better across all regions without introducing bias from rare categories.

Answer

One-hot encode the region field using 50+ dummy variables.

Answer

Remove the region field to avoid bias.

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Use region as a numeric rank based on past conversion rates.

Question 15

Universal Containers (UC) uses Einstein Lead Scoring to prioritize leads. They have 500,000 leads in the system. Recently, the model scores have been inconsistent: some leads with low activity receive high scores, while active leads score low. The model was trained 3 months ago. UC updates lead records daily via an external system, but the data is often incomplete (e.g., missing company size). Support has reported slow performance on lead views. The admin notices that the 'Data Refresh Status' for Einstein Lead Scoring shows 'Pending' for 2 weeks. UC wants to improve model accuracy and performance.

Which action should the admin take first?

Accepted Answer

Schedule a data transform to cleanse and refresh lead data before model training. The 'Data Refresh Status' has been 'Pending' for two weeks, indicating that the model is not receiving updated lead data. Scheduling a data transform to cleanse and refresh lead data before model training directly addresses the root cause: incomplete and stale data (e.g., missing company size) leads to inconsistent scores. This action ensures the model trains on clean, current data, improving both accuracy and performance.

Answer

Increase data retention period for lead records

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Rebuild the Einstein Lead Scoring model using different fields

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Increase the model training frequency to weekly

Question 16

An admin is configuring Einstein Lead Scoring. They want to ensure the lead score is visible in list views and reports. Which TWO settings or actions are required?

Accepted Answer

Add the Lead Score field to the list view layout. The Lead Score field must be added to the list view layout to make it visible in list views and reports. Without adding the field to the layout, users cannot see the score in those contexts, even if scoring is enabled.

Answer

Run a lead scoring batch job manually

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Create a custom report type for Lead Score

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Assign the 'View Lead Score' permission to users

Question 17

Which Salesforce Einstein feature provides automated statistical analysis of data, generates stories in natural language, and offers improvement suggestions in a waterfall chart?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct answer because it is the Salesforce AI feature specifically designed to perform automated statistical analysis on data, generate natural language narratives (stories) that explain key insights, and provide actionable improvement suggestions visualized in a waterfall chart. Unlike other Einstein features, Discovery focuses on surfacing hidden patterns and recommending specific actions to improve business outcomes.

Answer

Einstein GPT

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Einstein Analytics

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Einstein Prediction Builder

Question 18

A service team uses Einstein Discovery to analyze customer churn. The story shows 'Average Resolution Time' is a key driver. What is the best action?

Accepted Answer

Reduce Average Resolution Time through process changes. Einstein Discovery identifies drivers; reducing resolution time directly addresses root cause. Custom reports just show data, flows send alerts, processes update records but don't reduce time.

Answer

Update account records via process builder

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Configure a flow to send churn alerts

Answer

Create a custom report on churn

Question 19

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

Answer

Generating report charts and dashboards.

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Creating workflow rules based on user behavior.

Question 20

A company wants to use Einstein Bots to handle customer inquiries. They need to train the bot to understand different customer intents. Which TWO components are essential for defining bot understanding?

Accepted Answer

Intents. Intents are essential because they define the purpose or goal of a customer's input, such as 'Check Order Status' or 'Cancel Subscription'. The bot uses intents to classify user messages and determine the appropriate response or action. Without intents, the bot cannot understand what the customer wants, making them a foundational component of natural language understanding (NLU) in Einstein Bots.

Answer

Actions

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Topics

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Dialogue flows