Salesforce Salesforce Agentforce Specialist Practice Exams
Last updated on Oct 22,2026- Exam Code: Salesforce Agentforce Specialist
- Exam Name: Salesforce Agentforce Specialist
- Certification Provider: Salesforce
- Latest update: Oct 22,2026
Question #141
Universal Containers deployed the new Agentforce Sales Development Representative (SDR) Into
production, but sales reps are saying they can’t find it.
What is causing this issue?
- A . Sales rep users profiles are missing the Allow SDR Agent permission.
- B . Sales rep users do not have access to the SDR Agent object.
- C . Sales rep users are missing the Use SDR Agent permission set.
Correct Answer: C
C
Explanation:
Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?
If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.
Key Considerations for This Issue:
Permission Set Restriction
Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.
o If they lack this permission, the SDR Agent will not appear in their interface.
Agentforce Role-Based Access Control
Agentforce assigns specific permissions based on user roles.
o Sales reps require explicit permission to access the SDR Agent.
Fixing the Issue
The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales
reps.
o This is done in Setup → Permission Sets → Assign to Users.
Why Not the Other Options?
C
Explanation:
Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?
If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.
Key Considerations for This Issue:
Permission Set Restriction
Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.
o If they lack this permission, the SDR Agent will not appear in their interface.
Agentforce Role-Based Access Control
Agentforce assigns specific permissions based on user roles.
o Sales reps require explicit permission to access the SDR Agent.
Fixing the Issue
The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales
reps.
o This is done in Setup → Permission Sets → Assign to Users.
Why Not the Other Options?
Question #142
Before activating a custom copilot action, An Agentforce would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.
Which tool should the Agentforce Specialist recommend?
- A . Model Playground
- B . Agent
- C . Copilot Builder
Correct Answer: C
C
Explanation:
To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows Agentforce Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability to test, refine, and improve actions based on real-world utterances.
Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.
Option A (Model Playground) is used for testing models, not user utterances.
Option B (Agent) refers to the conversational interface but isn’t the right tool for designing and testing actions.
Reference: Salesforce Copilot Builder Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm
C
Explanation:
To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows Agentforce Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability to test, refine, and improve actions based on real-world utterances.
Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.
Option A (Model Playground) is used for testing models, not user utterances.
Option B (Agent) refers to the conversational interface but isn’t the right tool for designing and testing actions.
Reference: Salesforce Copilot Builder Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm
Question #143
A support team handles a high volume of chat interactions and needs a solution to provide quick, relevant responses to customer inquiries.
Responses must be grounded in the organization’s knowledge base to maintain consistency and accuracy.
Which feature in Einstein for Service should the support team use?
- A . Einstein Service Replies
- B . Einstein Reply Recommendations
- C . Einstein Knowledge Recommendations
Correct Answer: B
B
Explanation:
The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.
Einstein Service Replies (Option A) is focused on generating replies but doesn’t have the same emphasis on grounding responses in the knowledge base.
Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.
Salesforce Agentforce Specialist
Reference: For more information on Einstein Reply Recommendations:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm
B
Explanation:
The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.
Einstein Service Replies (Option A) is focused on generating replies but doesn’t have the same emphasis on grounding responses in the knowledge base.
Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.
Salesforce Agentforce Specialist
Reference: For more information on Einstein Reply Recommendations:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm
Question #144
Universal Containers has a new AI project.
What should An Agentforce consider when adding a related list on the Account object to be used in the prompt template?
- A . After selecting a related list from the Account, use the field picker to choose merge fields in Prompt Builder.
- B . Prompt Builder must be used to assign the fields from the related list as a JSON format.
- C . The fields for the related list are based on the default page layout of the Account for the current user.
Correct Answer: A
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 Agentforce 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 Agentforce Specialist Study Guide
Outlines best practices for referencing related records and fields in generative AI prompts.
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 Agentforce 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 Agentforce Specialist Study Guide
Outlines best practices for referencing related records and fields in generative AI prompts.
Question #145
Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC wishes to create a digest of account action plans using the generative API feature.
Which API service should UC use to meet this requirement?
- A . REST API
- B . Metadata API
- C . SOAP API
Correct Answer: A
A
Explanation:
To create a digest of account action plans using the generative API feature, Universal Containers should use the REST API. The REST API is ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.
Metadata API is used for retrieving and deploying metadata, not for data operations like generating summaries.
SOAP API is an older API used for integration but is less flexible compared to REST for this specific use case.
For more details, refer to Salesforce REST API documentation regarding using REST for data integration and generating content.
A
Explanation:
To create a digest of account action plans using the generative API feature, Universal Containers should use the REST API. The REST API is ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.
Metadata API is used for retrieving and deploying metadata, not for data operations like generating summaries.
SOAP API is an older API used for integration but is less flexible compared to REST for this specific use case.
For more details, refer to Salesforce REST API documentation regarding using REST for data integration and generating content.
Question #146
What is automatically created when a custom search index is created in Data Cloud?
- A . A retriever that shares the name of the custom search index.
- B . A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.
- C . A predefined Apex retriever class that can be edited by a developer to meet specific needs.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce Data Cloud, a custom search index is created to enable efficient retrieval of data (e.g., documents, records) for AI-driven processes, such as grounding Agentforce responses. Let’s evaluate the options based on Data Cloud’s functionality.
Option A: A retriever that shares the name of the custom search index.
When a custom search index is created in Data Cloud, a corresponding retriever is automatically generated with the same name as the index. This retriever leverages the index to perform contextual searches (e.g., vector-based lookups) and fetch relevant data for AI applications, such as Agentforce prompt templates. The retriever is tied to the indexed data and is ready to use without additional configuration, aligning with Data Cloud’s streamlined approach to AI integration. This is explicitly documented in Salesforce resources and is the correct answer.
Option B: A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.
While dynamic behavior sounds appealing, there’s no concept of a "dynamic retriever" in Data Cloud that adjusts parameters at runtime without configuration. Retrievers are tied to specific indexes and operate based on predefined settings established during index creation. This option is not supported by official documentation and is incorrect.
Option C: A predefined Apex retriever class that can be edited by a developer to meet specific needs.
Data Cloud does not generate Apex classes for retrievers. Retrievers are managed within the Data Cloud platform as part of its native AI retrieval system, not as customizable Apex code. While developers can extend functionality via Apex for other purposes, this is not an automatic outcome of creating a search index, making this option incorrect.
Why Option A is Correct:
The automatic creation of a retriever named after the custom search index is a core feature of Data Cloud’s search and retrieval system. It ensures seamless integration with AI tools like Agentforce by providing a ready-to-use mechanism for data retrieval, as confirmed in official documentation.
Reference: Salesforce Data Cloud Documentation: Custom Search Indexes C States that a retriever is auto-created with the same name as the index.
Trailhead: Data Cloud for Agentforce C Explains retriever creation in the context of search indexes.
Salesforce Help: Set Up Search Indexes in Data Cloud C Confirms the retriever-index relationship.
A
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce Data Cloud, a custom search index is created to enable efficient retrieval of data (e.g., documents, records) for AI-driven processes, such as grounding Agentforce responses. Let’s evaluate the options based on Data Cloud’s functionality.
Option A: A retriever that shares the name of the custom search index.
When a custom search index is created in Data Cloud, a corresponding retriever is automatically generated with the same name as the index. This retriever leverages the index to perform contextual searches (e.g., vector-based lookups) and fetch relevant data for AI applications, such as Agentforce prompt templates. The retriever is tied to the indexed data and is ready to use without additional configuration, aligning with Data Cloud’s streamlined approach to AI integration. This is explicitly documented in Salesforce resources and is the correct answer.
Option B: A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.
While dynamic behavior sounds appealing, there’s no concept of a "dynamic retriever" in Data Cloud that adjusts parameters at runtime without configuration. Retrievers are tied to specific indexes and operate based on predefined settings established during index creation. This option is not supported by official documentation and is incorrect.
Option C: A predefined Apex retriever class that can be edited by a developer to meet specific needs.
Data Cloud does not generate Apex classes for retrievers. Retrievers are managed within the Data Cloud platform as part of its native AI retrieval system, not as customizable Apex code. While developers can extend functionality via Apex for other purposes, this is not an automatic outcome of creating a search index, making this option incorrect.
Why Option A is Correct:
The automatic creation of a retriever named after the custom search index is a core feature of Data Cloud’s search and retrieval system. It ensures seamless integration with AI tools like Agentforce by providing a ready-to-use mechanism for data retrieval, as confirmed in official documentation.
Reference: Salesforce Data Cloud Documentation: Custom Search Indexes C States that a retriever is auto-created with the same name as the index.
Trailhead: Data Cloud for Agentforce C Explains retriever creation in the context of search indexes.
Salesforce Help: Set Up Search Indexes in Data Cloud C Confirms the retriever-index relationship.
Question #147
The sales team at a hotel resort would like to generate a guest summary about the guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page.
Which AI capability should the team use?
- A . Model Builder
- B . Agent Builder
- C . Prompt Builder
Correct Answer: C
C
Explanation:
Comprehensive and Detailed In-Depth
The hotel resort team needs an AI-generated guest summary with recommendations, displayed exclusively on the contact record page. Let’s assess the options.
Option A: Model Builder
Model Builder in Salesforce creates custom predictive AI models (e.g., for scoring or classification) using Data Cloud or Einstein Platform data. It’s not designed for generating text summaries or embedding them on record pages, making it incorrect.
Option B: Agent Builder
Agent Builder in Agentforce Studio creates autonomous AI agents for tasks like lead qualification or customer service. While agents can provide summaries, they operate in conversational interfaces (e.g., chat), not as static content on a record page. This doesn’t meet the location-specific requirement, making it incorrect.
Option C: Prompt Builder
Einstein Prompt Builder allows creation of prompt templates that generate text (e.g., summaries, recommendations) using Generative AI. The template can pull data from contact records (e.g., activity preferences) and be embedded as a Lightning component on the contact record page via a Flow or Lightning App Builder. This ensures the summary is available only where specified, meeting
the team’s needs perfectly and making it the correct answer.
Why Option C is Correct:
Prompt Builder’s ability to generate contextual summaries and integrate them into specific record pages via Lightning components aligns with the team’s requirements, as supported by Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Prompt Builder > Embedding Prompts C Details placement on record pages.
Trailhead: Build Prompt Templates in Agentforce C Covers summaries from object data.
Salesforce Help: Customize Record Pages with AI C Confirms Prompt Builder integration.
C
Explanation:
Comprehensive and Detailed In-Depth
The hotel resort team needs an AI-generated guest summary with recommendations, displayed exclusively on the contact record page. Let’s assess the options.
Option A: Model Builder
Model Builder in Salesforce creates custom predictive AI models (e.g., for scoring or classification) using Data Cloud or Einstein Platform data. It’s not designed for generating text summaries or embedding them on record pages, making it incorrect.
Option B: Agent Builder
Agent Builder in Agentforce Studio creates autonomous AI agents for tasks like lead qualification or customer service. While agents can provide summaries, they operate in conversational interfaces (e.g., chat), not as static content on a record page. This doesn’t meet the location-specific requirement, making it incorrect.
Option C: Prompt Builder
Einstein Prompt Builder allows creation of prompt templates that generate text (e.g., summaries, recommendations) using Generative AI. The template can pull data from contact records (e.g., activity preferences) and be embedded as a Lightning component on the contact record page via a Flow or Lightning App Builder. This ensures the summary is available only where specified, meeting
the team’s needs perfectly and making it the correct answer.
Why Option C is Correct:
Prompt Builder’s ability to generate contextual summaries and integrate them into specific record pages via Lightning components aligns with the team’s requirements, as supported by Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Prompt Builder > Embedding Prompts C Details placement on record pages.
Trailhead: Build Prompt Templates in Agentforce C Covers summaries from object data.
Salesforce Help: Customize Record Pages with AI C Confirms Prompt Builder integration.
Question #148
Universal Containers’ data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?
- A . Model Builder
- B . App Builder
- C . Copilot Builder
Correct Answer: A
A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform’s native AI capabilities.
Option B, App Builder, is primarily used to build and configure applications in Salesforce, not
to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce Agentforce Specialist
Reference: For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform’s native AI capabilities.
Option B, App Builder, is primarily used to build and configure applications in Salesforce, not
to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce Agentforce Specialist
Reference: For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
Question #149
Once a data source is chosen for an Agentforce Data Library, what is true about changing that data source later?
- A . The data source can be changed through the Data Cloud settings.
- B . The Data Retriever can be reconfigured to use a different data source.
- C . The data source cannot be changed after it is selected.
Correct Answer: C
C
Explanation:
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce’s AI-driven environment.
Key Considerations in Agentforce Data Library
Data Source Lock-In
The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.
o Any updates or modifications require creating a new Data Library instead of modifying the existing one.
Why Can’t the Data Source Be Changed?
The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.
Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.
Workarounds for Changing Data Sources
If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.
o Old data can be manually migrated into the new data source for continuity.
Why Not the Other Options?
C
Explanation:
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce’s AI-driven environment.
Key Considerations in Agentforce Data Library
Data Source Lock-In
The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.
o Any updates or modifications require creating a new Data Library instead of modifying the existing one.
Why Can’t the Data Source Be Changed?
The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.
Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.
Workarounds for Changing Data Sources
If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.
o Old data can be manually migrated into the new data source for continuity.
Why Not the Other Options?
Question #150
Universal Containers has seen a high adoption rate of a new feature that uses generative AI to populate a summary field of a custom object, Competitor Analysis. All sales users have the same profile but one user cannot see the generative AlI-enabled field icon next to the summary field.
What is the most likely cause of the issue?
- A . The user does not have the Prompt Template User permission set assigned.
- B . The prompt template associated with summary field is not activated for that user.
- C . The user does not have the field Generative AI User permission set assigned.
Correct Answer: C
C
Explanation:
In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.
Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missing Generative AI User permission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won’t be accessible.
Why not A? The Prompt Template User permission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it’s not directly related to the visibility of AI-enabled field icons.
Why not B? While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.
For more detailed information, you can review Salesforce documentation on permission sets related to AI capabilities at Salesforce AI Documentation and Einstein GPT permissioning guidelines.
C
Explanation:
In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.
Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missing Generative AI User permission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won’t be accessible.
Why not A? The Prompt Template User permission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it’s not directly related to the visibility of AI-enabled field icons.
Why not B? While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.
For more detailed information, you can review Salesforce documentation on permission sets related to AI capabilities at Salesforce AI Documentation and Einstein GPT permissioning guidelines.