Questions for the SALESFORCE AI SPECIALIST were updated on : Dec 01 ,2025
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?
C
Explanation:
Context of the Question
Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content,
responses) using AI models.
The question presents three potential use cases, asking which one best fits the capabilities of
Einstein Prompt Builder.
Einstein Prompt Builder Typical Use Cases
Text Generation & Summaries: Great for writing or summarizing content, like responding to an email
or generating text for a record field.
Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?
(Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found
in Einstein Discovery or standard reporting, not generative text solutions.
(Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an
Einstein Discovery or Lead Scoring scenario.
Sending a Personalized RFP Email (Option C) is a classic example of using generative AI to compose
well-structured, context-aware text.
Conclusion
Option C (Send reply to a request for proposal via a personalized email) is the best match for Einstein
Prompt Builder’s generative text functionality.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation:
Einstein Prompt Builder Overview
Highlights how to use Prompt Builder to create and customize text-based responses, especially for
email or record fields.
Salesforce AI Specialist Study Guide
Explains that generative AI features in Salesforce are designed for creating or summarizing text, not
for advanced predictive use cases (like forecasting or lead scoring).
Universal Containers wants to incorporate CRM data as well-formatted JSON in a prompt to a large
language model (LLM).
What is an important consideration for this requirement?
B
Explanation:
Context of the Question
Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language
model (LLM).
The question is about an important technical or design consideration for including CRM data as JSON
in that prompt.
Why Apex Code for JSON Formatting?
Apex to Generate JSON: Salesforce does not have a simple “checkbox” or single setting to “convert
CRM data to JSON.” Typically, to structure data as JSON in a template, you either:
Use an Apex class that queries or processes the data, then returns a JSON string.
Use a Flow or formula approach (though complex data structures often require Apex).
No Built-In “Enable JSON Format in Prompt Builder”: Prompt Builder doesn’t have a toggle that
automatically transforms data into JSON.
Conclusion
The practical solution to pass CRM data in JSON format to an LLM is to use Apex code (or a
specialized Flow approach) to produce a JSON string, which the prompt can then merge and pass
along. Hence, Option B is correct.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation:
Working with JSON in Apex
Describes how to serialize and deserialize data using Apex for integration or AI prompts.
Salesforce AI Specialist Study Guide
Emphasizes the need for custom logic (often in Apex) when complex data transformations (like JSON
formatting) are required.
How does an Agent respond when it can't understand the request or find any requested information?
C
Explanation:
Context of the Question
When an Agent (e.g., Agentforce or a similar generative AI assistant in Salesforce) cannot understand
a user’s request or fails to locate the requested information, it should provide a coherent fallback.
Fallback / Error Handling in Agentforce
Preconfigured or “Fallback” Message: Typically, within the setup of any AI assistant, admins define a
fallback or “failure” response. This message can be tailored to each action type or scenario (e.g., “No
data found,” “Sorry, I didn’t get that—please try again,” etc.).
Why Not an Automatically Generated Error Message? (Option A) AI assistants rarely show raw
system error messages; they generally display friendly, standardized messages.
Why Not a Purely Generic “Rephrase” Message? (Option B) Although an agent might prompt the
user to rephrase, Salesforce best practices are to configure a fallback response or fallback action that
aligns with the brand and the context. This is typically a “preconfigured message based on the action
type.”
Conclusion
Because the assistant uses a preconfigured fallback that is set up in the environment (for example, in
the agent’s or domain’s settings), the correct choice is Option C.
Salesforce AI Specialist Reference & Documents
Salesforce Pilot / Agentforce Setup Documentation
Explains how to configure a fallback or default message when the AI cannot fulfill a user’s request.
Salesforce AI Specialist Study Guide
Details best practices for AI-driven assistants and how fallback scenarios are handled with
preconfigured messages.
Universal Containers (UC) plans to automatically populate the Description field on the Account
object.
Which type of prompt template should UC use?
A
Explanation:
Context of the Question
Universal Containers (UC) wants to automatically populate the Description field on the Account
object. The AI-driven solution must generate textual data and write it directly into a field.
Field Generation Prompt Template
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in
fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the
instructions, data inputs, and desired output for the AI. The resulting text then populates the
specified field, such as the Account Description.
Why Not Flex or Sales Email Prompt Templates?
Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references
from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward
text generation on a single field.
Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting
outreach or follow-up messages). This template is not specifically built to populate a field on a
record.
Conclusion
For automatically populating the Description field with AI-generated content, the Field Generation
prompt template (Option A) is the correct choice.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation:
Prompt Template Types
Explains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.
Salesforce AI Specialist Study Guide
Highlights Field Generation prompt templates for populating or updating record fields with AI-
generated text.
Universal Containers wants support agents to use Agentforce to ask questions about its product
tutorials and product guides.
What should the AI Specialist do to meet this requirement?
C
Explanation:
Context of the Question
Universal Containers (UC) wants its support agents to use Agentforce to ask questions about product
tutorials and product guides. Agentforce typically references knowledge sources to provide accurate
and contextual responses.
Why Knowledge Articles?
Centralized Repository: Publishing product tutorials and guides as Knowledge articles in Salesforce
ensures that the information is readily available and searchable by Agentforce.
AI Integration: Salesforce’s AI solutions, including Agentforce, can often be configured to pull content
directly from Salesforce Knowledge articles, giving users on-demand answers without manual data
duplication.
Maintenance & Updates: Storing content in Salesforce Knowledge simplifies content updates,
versioning, and user permissions.
Why Not the Other Options?
Option A (Create a Prompt Template): Creating a prompt template alone does not solve how the
underlying content (tutorials, guides) is stored or accessed by Agentforce. Prompt templates shape
the queries/responses but do not provide the knowledge base.
Option B (Add an Answer Questions Custom Field): A single field on the product object is insufficient
for the depth of information found in tutorials and guides. It also lacks the robust search and user-
friendly interface that Knowledge articles provide.
Conclusion
To ensure Agentforce can effectively retrieve and deliver accurate information about products,
publishing product tutorials and guides as Knowledge articles is the recommended approach.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation:
Set Up Salesforce Knowledge
Discusses how to publish articles for easy access by AI-driven assistants and support teams.
Salesforce AI Specialist Study Guide
Explains best practices for feeding knowledge sources to generative AI and Agentforce.
Which configuration must an AI Specialist complete for users to access generative Al-enabled fields
in the Salesforce mobile app?
C
Explanation:
Context of the Question
Universal Containers (UC) has generative AI–enabled fields that users can access in the desktop
experience.
The AI Specialist needs these same fields to be visible and usable in the Salesforce Mobile App.
Why Dynamic Forms on Mobile?
Dynamic Forms allow you to configure record pages so that fields and sections can appear or be
hidden based on certain criteria.
When you enable “Dynamic Forms for Mobile,” any generative AI–enabled fields placed on the
dynamic layout become accessible in the Salesforce mobile experience.
There is no standard Setup option labeled “Enable Mobile Generative AI” or “Enable Mobile Prompt
Responses” as a universal toggle; the existing official approach is to ensure dynamic forms (and the
relevant fields) are supported on mobile.
Conclusion
Ensuring that these AI-driven fields are visible on mobile is accomplished by turning on Dynamic
Forms on Mobile and adding those fields to the dynamic layout. Therefore, Option C is correct.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation:
Dynamic Forms Overview
Explains how to enable Dynamic Forms for both desktop and mobile UIs, allowing newly added fields
(including generative AI–enabled ones) to display in the Salesforce Mobile App.
Salesforce AI Specialist Study Guide
Reiterates that to expose generative AI fields or components in mobile, you must configure dynamic
forms and ensure compatibility on mobile layouts.
Universal Container (UC) has effectively utilized prompt templates to update summary fields on
Lightning record pages. An admin now wishes to incorporate similar functionality into UC's
automation process using Flow.
How can the admin get a response from this prompt template from within a flow to use as part of
UC's automation?
B
Explanation:
Context of the Question
Universal Container (UC) has used prompt templates to update summary fields on record pages.
Now, the admin wants to incorporate similar generative AI functionality within a Flow for
automation purposes.
How to Call a Prompt Template Within a Flow
Flow Action: Salesforce provides a standard way to invoke generative AI templates or prompts within
a Flow step. From the Flow Builder, you can add an “Action” that references the prompt template you
created in Prompt Builder.
Other Options:
Invocable Apex: Possible fallback if there’s no out-of-the-box Flow Action available. However,
Salesforce is releasing native Flow integration for AI prompts, making custom Apex less necessary.
Einstein for Flow: A broad label for Salesforce’s generative AI features within Flow. Under the hood,
you typically use a “Flow Action” that points to your prompt.
Conclusion
The easiest out-of-the-box solution is to use a Flow Action referencing the prompt template. Hence,
Option B is correct.
Salesforce AI Specialist Reference & Documents
Salesforce Trailhead:
Use Prompt Templates in Flow
Demonstrates how to add an Action in Flow that calls a prompt template.
Salesforce Documentation:
Einstein GPT for Flow
Explains standard flow actions to invoke and handle generative AI responses.
Universal Containers (UC) is building a Flex prompt template. UC needs to use data returned by the
flow in the prompt template.
Which flow element should UC use?
C
Explanation:
Context of the Question
Universal Containers (UC) wants to build a Flex prompt template that uses data returned by a Flow.
“Flex Prompt Templates” allow admins and AI Specialists to incorporate external or dynamic data
into generative AI prompts.
Why “Add Flow Instructions” Is Needed
Passing Flow Data into Prompt Templates: When configuring the prompt, you must specify how data
from the running Flow is passed into the Flex template. The designated element for that is typically
“Flow Instructions,” which map the Flow outputs to the prompt.
Other Options:
Add Flex Instructions: Typically controls how the AI responds or structures the output, not how to
bring Flow data into the template.
Add Prompt Instructions: Usually for static or manual instructions that shape the AI’s response,
rather than referencing dynamic data from the Flow.
Outcome
“Add Flow Instructions” ensures the prompt can dynamically use the data that the Flow returns—
making Option C correct.
Salesforce AI Specialist Reference & Documents
Salesforce Help & Training:
Using Prompt Templates with Flow
Explains how to pass Flow variables into a prompt template via a specialized step (e.g., “Flow
Instructions”).
Salesforce AI Specialist Study Guide
Outlines how to configure generative AI prompts that reference real-time Flow data.
Universal Containers has a strict change management process that requires all possible configuration
to be completed in a sandbox which will be deployed to production. The AI Specialist is tasked with
setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in
production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should the AI Specialist take in the sandbox that can be deployed to
the production org?
C
Explanation:
Context of the Question
Universal Containers (UC) has a strict change management process that requires all possible
configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the “Einstein Work Summaries”
permission set is already available in Production.
The AI Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to
a change set or deployment package.
Why Option C is Correct
No Need to Turn on Einstein in Sandbox for Deployment: Einstein Generative AI is already enabled in
Production; turning it on in the sandbox is typically a manual step if you want to test, but that step
itself is not “deployable” in the sense of metadata.
Permission Set Assignments (as in Option A) are not deployable metadata. You can deploy the
Permission Set itself but not the specific user assignments. Since the question specifically asks
“Which other configuration steps should be taken in the sandbox that can be deployed to the
production org?”, user assignment is not one of them.
Why Not Option A or B?
Option A: Mentions creating permission set assignments for agents. This cannot be directly deployed
from sandbox to Production, as permission set assignments are user-specific and considered “data,”
not metadata.
Option B: Mentions “Turn on Einstein.” But Einstein Generative AI is already enabled in Production.
Additionally, “Turning on Einstein” is typically an org-level setting, not a deployable metadata item.
Conclusion
The main deployable items you can reliably create and test in a sandbox, and then migrate to
Production, are:
Custom Fields (Issue, Resolution, Summary).
A Quick Action that updates those fields.
Page Layout Change to include the Wrap Up component.
Therefore, Option C is correct and focuses on actions that are truly deployable as metadata from a
sandbox to Production.
Salesforce AI Specialist Reference & Documents
Salesforce Trailhead:
Work Summaries with Einstein GPT
Provides an overview of how to configure Work Summaries, including the need for custom fields,
quick actions, and UI components.
Salesforce Documentation:
Deploying Metadata Between Orgs
Explains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick
actions vs. user permission set assignments).
Salesforce AI Specialist Study Guide
Outlines which Einstein Generative AI and Work Summaries configurations are deployable as
metadata.
Universal Containers implemented Agentforce for its users. One user complains that an Agent is not
deleting activities from the past 7 days. What is the reason for this issue?
C
Explanation:
Context of the Question
Universal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user
reports that an Agent is unable to delete recent activities.
Why Agentforce Cannot Delete Records
Agentforce’s Standard Actions: Agentforce typically has predefined or “standard” actions like Create,
Update, or Summarize records. However, a standard Delete Record action is not part of the default
set of Agentforce actions.
Implication: If Agentforce has no built-in delete functionality, it cannot remove activities—even if the
user has permission to delete them in the Salesforce UI.
Why Other Options Are Incorrect
Option A – Permission to Delete the User’s Records: Standard Salesforce user permissions do not
automatically extend to Agentforce’s capabilities. Even if the user can delete records, that doesn’t
grant Agentforce a new action.
Option B – Agentforce Delete Record Action Permission: There is no separate “Delete Record Action
permission” for Agentforce to be toggled. The relevant issue is that the standard Delete Record
action does not exist within Agentforce out of the box.
Conclusion
The core reason for the issue is that Agentforce does not support a standard Delete Record action
(Choice C).
Salesforce AI Specialist Reference & Documents
Salesforce Official Documentation – Agentforce
(Note: Agentforce may be a pilot or specialized feature; check pilot release notes or official docs for
standard actions.)
Salesforce AI Specialist Study Guide
Covers the limitations of certain AI-enabled features regarding record operations.
Universal Containers has a new AI project.
What should an AI Specialist consider when adding a related list on the Account object to be used in
the prompt template?
A
Explanation:
Context of the Question
Universal Containers (UC) wants to include details from a related list on the Account object in a
prompt template. This is typically done via Prompt Builder in Salesforce’s generative AI setup.
Prompt Builder Behavior
Selecting a Related List: Within Prompt Builder, you can navigate to the object (Account) and choose
which related list (e.g., Contacts, Opportunities) you want to reference.
Field Picker: Once a related list is chosen, Prompt Builder provides a field picker interface, allowing
you to select specific fields from that related list. These fields then become available for merge fields
or dynamic insertion within your prompt.
Why Option A is Correct
Direct Alignment with the Standard Process: The recommended approach in Salesforce’s
documentation is to select a related list and then use the field picker to add the necessary fields into
your AI prompt. This ensures the prompt has exactly the data you need from that related list.
Why Not Option B (JSON Formatting)
No Mandatory JSON Requirement: Although you can structure data as JSON if you desire advanced
formatting, Prompt Builder does not require you to manually assign the fields from the related list in
JSON. The platform automatically handles how the data is passed along in the background.
Why Not Option C (Default Page Layout)
Independent of Page Layout: Prompt Builder does not rely strictly on the default page layout for
fields. You can configure the fields you want from the related list, independent of how the user’s
page layout is set up in the UI.
Conclusion
Since the official Salesforce approach involves selecting a related list and then using the field picker
to insert merge fields, Option A is the correct and verified answer.
Salesforce AI Specialist Reference & Documents
Salesforce Official Documentation:
Prompt Builder Basics
Explains how to reference objects and related lists when building AI prompts.
Salesforce Trailhead:
Get Started with Prompt Builder
Provides hands-on exercises demonstrating how to pick fields from related objects or lists.
Salesforce AI Specialist Study Guide
Outlines best practices for referencing related records and fields in generative AI prompts.
Universal Containers (UC) wants to improve the productivity of its sales team with generative AI
technology. However, UC is concerned that public AI virtual assistants lack adequate company data
to general useful responses.
Which solution should UC consider?
A
Explanation:
Context of the Questio n: Universal Containers (UC) wants to harness generative AI to boost sales
productivity. They are wary of public AI virtual assistants (like generic chatbots) that lack sufficient
UC-specific data to generate useful business responses.
Why Fine-Tune an Einstein AI Model with CRM Data?
Company-Specific Relevance: By fine-tuning Einstein AI with UC’s CRM data (accounts, opportunities,
products, and historical interactions), the model learns the enterprise-specific context. This ensures
that the generative outputs are accurate and tailored to UC’s sales scenarios.
Security and Compliance: Using Salesforce Einstein within the Salesforce ecosystem keeps data under
UC’s control, aligning with trust, security, and compliance requirements.
Better Predictions: Einstein AI can produce more relevant insights (e.g., recommended next steps,
content suggestions, or AI-generated email responses) when it has been trained on real, high-quality
internal data.
Why Not Build an AI Model with Einstein Discovery (Option B)?
Einstein Discovery Use Case: Einstein Discovery is best suited for predictive and prescriptive analytics
(e.g., analyzing large data sets for patterns, scoring leads, or predicting churn). While it provides
advanced analytics, it is not primarily designed for generative text-based interactions for end-user
consumption in a conversational format.
Why Not Enable Agentforce (Option C)?
Agentforce Overview: “Agentforce” (sometimes referencing a pilot or non-mainstream name)
typically focuses on interactive help or workforce collaboration. It does not inherently solve the
problem of large-scale generative AI using internal CRM data. Moreover, you still need a robust
generative engine fine-tuned on company data.
Outcome: Fine-tuning the Einstein AI model with UC’s CRM data (Answer A) is the most direct,
Salesforce-native solution to provide generative AI responses that are aligned with UC’s context,
driving productivity gains and ensuring data privacy.
Salesforce AI Specialist References & Documents
Salesforce Official: Einstein GPT Overview
Discusses how Einstein GPT can be fine-tuned with specific CRM data to deliver contextually
relevant, generative AI responses.
Salesforce Trailhead:
Get Started with Salesforce Einstein
Explains the fundamentals of AI within the Salesforce platform, including training and optimizing
Einstein models.
Salesforce Documentation: Einstein Discovery
Details how Einstein Discovery is primarily used for advanced analytics and predictions, not direct
generative text solutions.
Salesforce AI Specialist Study Guide
Provides the official outline of Einstein AI capabilities, referencing how to configure and fine-tune
models for specialized enterprise use cases.
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings
recorded by its sales team.
What should the AI Specialist be aware of before enabling this feature?
C
Explanation:
Before enabling Call Explorer, the Salesforce AI Specialist must ensure that the Einstein Conversation
Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein
Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and
actionable insights.
Key Considerations:
Permission Set Requirement:
Users (including admins) need the Einstein Conversation Insights permission set to access and use
Call Explorer. Without this, the feature remains inaccessible.
The permission set grants access to ECI tools, including call transcription, analysis, and dashboard
visibility.
Why Other Options Are Incorrect:
A . Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce
Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission set,
not Knowledge configuration.
B . Custom Actions: Call Explorer does not require custom actions to be built before configuration. It
is a pre-built analytics tool that works once permissions and data sources (e.g., call recordings) are
configured.
Reference:
Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation
Insights permission set is required to access Call Explorer.
Trailhead Module: "Einstein Conversation Insights Basics" outlines permission prerequisites for
enabling call analytics.
Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI
permissions.
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
C
Explanation:
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action
Instructions. Here's why:
Action Instructions define the logic, parameters, and steps the AI should follow to execute the action.
They include:
How input data is processed.
API calls or Apex invocations.
Conditional logic (e.g., decision trees).
Testing and iterating on these instructions ensures alignment with the intended workflow. For
example, incorrect API endpoint references or misconfigured parameters in the instructions will
cause failures.
Action Input (Option A) refers to the data provided to the action. While validating input formats is
important, inputs are static once defined. The primary issue lies in whether the instructions
correctly use the inputs.
Action Name (Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the
"core logic" resides, requiring rigorous testing (Source:
Einstein Bots Developer Guide
).
Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle
edge cases and validate outputs (Source:
Trailhead
).
By iterating on Action Instructions, the AI Specialist ensures the action’s logic, integrations, and error
handling are robust.
What is a Salesforce AI Specialist able to configure in Data Masking within the Einstein Trust Layer?
C
Explanation:
In the Einstein Trust Layer, the Salesforce AI Specialist can configure privacy data entities to be
masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated
when processed by AI models.
Data Masking Configuration:
The AI Specialist defines which fields or data types (e.g., email, phone number, Social Security
Number) should be masked. For example, masking the Email field in a prompt response to protect
user privacy.
This is done through declarative settings in Salesforce, where entities (standard or custom fields) are
flagged for masking.
Why Other Options Are Incorrect:
A . Profiles exempt from masking: Exemptions are typically managed via permissions (e.g., field-level
security), not directly within Einstein Trust Layer’s Data Masking settings.
B . Encryption keys for masking: Encryption is separate from masking. Masking involves obfuscation
(e.g., replacing "
[email protected]
" with "@"), not encryption, which uses keys to secure data.
Reference:
Einstein Trust Layer Documentation: States that Data Masking allows admins to "define which fields
should be masked to protect sensitive data."
Trailhead Module: "Einstein Trust Layer Basics" explains configuring privacy entities for masking.
Salesforce Help Article: "Secure AI with Einstein Trust Layer" details masking configurations for
privacy compliance.