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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.
Topic 2	Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.
Topic 3	Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.
Topic 4	Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.
Topic 5	Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.

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Salesforce Certified AI Specialist Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
Universal Containers (UC) wants to enable its sales reps to explore opportunities that are similar to previously won opportunities by entering the utterance, "Show me other opportunities like this one." How should UC achieve this in Einstein Copilot?

A. Create a custom Copilot action calling a flow.
B. Create a custom Copilot action calling an Apex class.
C. Use the standard Copilot action.

Answer: C

Explanation:
Universal Containers can achieve the request to explore similar opportunities by using thestandard Copilot action.Einstein Copilothas built-in actions to handle natural language queries, such as "Show me other opportunities like this one." The standard action will process the query and return results based on predefined matching criteria like opportunity details and past Closed Won deals.
This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box functionality.
For further details, refer toEinstein Copilot for Sales documentationregarding standard actions and natural language processing.

NEW QUESTION # 138
How should an organization use theEinstein Trust layer to audit,track, and view masked data?

A. In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
B. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
C. Access the audit trail in Setup and export all user-generated prompts.

Answer: B

Explanation:
TheEinstein Trust Layeris designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
* Audit Trail in Data Cloud: Theaudit trailcaptures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. TheData Cloudprovides the infrastructure for managing and accessing this audit data.
* Why not B?UsingPrompt Builderin Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.
* Why not C?Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs.
More information on auditing AI interactions can be found in theSalesforce AI Trust Layerdocumentation, which outlines how organizations can manage and track generative AI interactions securely.

NEW QUESTION # 139
An AI Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities.
How should the AI Specialist gather the necessary data for the prompt template?

A. Select the latest Opportunities related list as a merge field.
B. Create a flow to retrieve the opportunity information.
C. Select the Account Opportunity object as a resource when creating the prompt template.

Answer: B

Explanation:
To gather the necessary data for populating the Latest Opportunities Summary custom field on the Account object with information from the three most recently opened opportunities, the AI Specialist should create a flow. A flow can be configured to query and retrieve the required opportunity records based on criteria such as their open date. Once the flow has gathered the necessary data, it can be used in a prompt template or other automation processes to populate the custom field on the Account record.
Option A is correct because creating a flow allows for dynamic data retrieval and control over the logic for selecting the most recent opportunities.
Option B and Option C do not provide sufficient control or data retrieval capabilities needed for this scenario.
Reference:
Salesforce Flow Documentation: https://help.salesforce.com/s/articleView?id=sf.flow.htm

NEW QUESTION # 140
An AI Specialist configured Data Masking within the Einstein Trust Layer.
How should the AI Specialist begin validating that the correct fields are being masked?

A. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
B. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
C. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.

Answer: C

Explanation:
To begin validating that the correct fields are being masked inEinstein Trust Layer, the AI Specialist should request theEinstein Generative AI Audit Datafrom theSecurity sectionof the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
* Option Bis correct because it allows for the retrieval of audit data that can be used to validate data masking.
* Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer.
References:
* Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm

NEW QUESTION # 141
A Salesforce Administrator is exploring the capabilities of Einstein Copilot to enhance user interaction within their organization. They are particularly interested in how Einstein Copilot processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Einstein Copilot directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries,facilitating a broad range of requests from users.
How does Einstein Copilot handle user requests In Salesforce?

A. Einstein Copilot will perform an HTTP callout to an LLM provider.
B. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
C. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.

Answer: B

Explanation:
Einstein Copilot is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Einstein Copilot analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.
OptionCaccurately describes this process. Einstein Copilot does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.
References:
* Salesforce AI Specialist Documentation - Einstein Copilot Overview:Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
* Salesforce Help - How Einstein Copilot Works:Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.

NEW QUESTION # 142
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