AI Associate AI Capabilities in CRM Practice Test 2 — 15 Questions

Question 1

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.

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Einstein Article Recommendations

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Einstein Bots

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Einstein Reply Recommendations

Question 2

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 3

Which TWO features are part of Einstein AI capabilities in Salesforce Sales Cloud?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is a core Einstein AI capability in Sales Cloud that uses predictive models to analyze historical data and assign a score to each opportunity, indicating its likelihood to close. This helps sales reps prioritize their efforts on deals most likely to convert, directly leveraging AI to enhance sales productivity.

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Einstein Case Classification

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Einstein Bots

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Einstein Article Recommendations

Question 4

Which THREE are requirements for enabling Einstein features in a Salesforce org?

Accepted Answer

An eligible Salesforce edition. Einstein features require an eligible Salesforce edition (e.g., Enterprise, Performance, or Unlimited) because the underlying AI infrastructure, including predictive models and data processing pipelines, is only available in these higher-tier editions. Without the correct edition, the necessary licenses and platform capabilities for Einstein are not provisioned.

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Custom objects must be created

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Activation of Einstein API in Setup

Question 5

Which TWO are best practices when implementing Einstein Recommendation Builder?

Accepted Answer

Use a dataset that reflects recent customer interactions. Einstein Recommendation Builder relies on recent customer interaction data to generate accurate and relevant product or content recommendations. Using stale or outdated data can lead to irrelevant suggestions, as the model learns from historical patterns that may no longer reflect current customer preferences or behaviors.

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Disable negative feedback to simplify the model

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Include all available data, even if it is old or unrelated

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Use a single dataset that contains the entire history of the org

Question 6

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

Accepted Answer

It predicts a binary outcome (e.g., convert or not). Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

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It predicts a numeric value

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It predicts the value of a text field

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It is currently retraining the model

Question 7

Refer to the exhibit. A Salesforce admin sees this error when trying to enable Einstein Lead Scoring. What should the admin do to resolve the issue?

Accepted Answer

Add more lead records with associated activities until reaching at least 100. Einstein Lead Scoring requires a minimum of 100 lead records with associated activities (e.g., emails, events, tasks) to generate a predictive model. The error indicates insufficient data, so adding more leads with activities meets the threshold for model training.

Answer

Enable Einstein features in the org

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Map lead fields to Einstein fields

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Grant the admin the 'Manage Einstein' permission

Question 8

Refer to the exhibit. A developer wrote a trigger to call an Einstein prediction API on lead insert. When new leads are created, the trigger fails with a 'Too many SOQL queries' error. What is the most likely cause?

Accepted Answer

The trigger is making API calls in a loop, causing governor limit issues. The trigger is making synchronous API calls to the Einstein prediction service inside a loop that iterates over each new lead. Each API call counts against the 'Number of callouts' governor limit (default 100 per transaction), and if more leads are inserted than the limit allows, the transaction fails with a 'Too many SOQL queries' error—though the error message is misleading, the root cause is exceeding the callout limit, not SOQL queries. The trigger should batch the API calls or use asynchronous processing to stay within limits.

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The trigger is executed before the lead is saved

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The trigger is querying a related object without an index

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The Score__c field is not writeable

Question 9

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.

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Workflow Rules

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Process Builder

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Email Templates

Question 10

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.

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Approval Processes

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Custom Formula Fields

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Data Export

Question 11

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

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Create a custom report on churn

Question 12

A company wants an AI chatbot that can handle customer inquiries about order status. Which tool should be configured?

Accepted Answer

Einstein Bot. Einstein Bot is the correct tool because it is Salesforce's native AI-powered chatbot designed to handle customer inquiries, including order status, through natural language processing and automated conversations. It can be configured to answer common questions, escalate complex issues, and integrate with backend systems to retrieve real-time order data without human intervention.

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Queues

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Omni-Channel

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Case Assignment Rules

Question 13

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.

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Post-Interaction Survey

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Reports

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Dashboards

Question 14

A legal firm wants to automate contract clause generation using AI with preapproved language. Which approach should they use?

Accepted Answer

Einstein GPT with Clause Library. Einstein GPT with Clause Library is specifically designed to generate contract clauses using preapproved language. It leverages generative AI combined with a library of approved legal clauses, ensuring compliance and consistency while automating clause creation within Salesforce.

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Email Templates

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Flow

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Document Templates

Question 15

A marketing team wants to predict which segment of customers will likely purchase a new product. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Segment Prediction. Einstein Segment Prediction uses predictive modeling and machine learning to analyze historical customer data and identify which segments are most likely to purchase a new product. It is specifically designed for predictive segmentation based on behavioral and demographic patterns, making it the most appropriate choice for this use case.

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Custom Reports

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List Views

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Campaigns