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NEW QUESTION # 25
Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles. When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let's troubleshoot the issue.
* Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.
* Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.Prompt templates in Prompt Builder don't require specific permissions beyond general Einstein Generative AI access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent's runtime access,not the template itself, making this incorrect.
* Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.
Why Option C is Correct:The Agentforce Service Agent User's lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Data Library Setup > Permissions- Requires Data Cloud access for agents.
* Trailhead: Ground Your Agentforce Prompts- Notes Data Cloud User permission for Knowledge grounding.
* Salesforce Help: Agentforce Security > Agent User Setup- Lists required permission sets.
NEW QUESTION # 26
Based on the user utterance, 'Show me all the customers in New York', which standard Agent action will the planner service use?
Answer: A
Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them. When a user issues a command like:"Show me all the customers in New York", the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
* Retrieves Data Based on Specific Field Values
* This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
* Uses standard or custom object fields (e.g., BillingState = 'New York').
* Works with Large Language Models (LLMs) and Copilot Actions
* When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
* Optimized for Structured Data Retrieval
* Ensures AI retrieves relevant CRM records quickly and accurately.
Why Not the Other Options?
# B. Fetch Records
* This is not a standard term in Einstein Copilot or Agentforce.
* No defined Agentforce action exists under this name.
# C. Select Records
* Select Records is used to pick records from an already presented list, not to retrieve them initially.
* If the user had already retrieved records and wanted to refine their selection, Select Records might be appropriate.
* However, since the user's request is to retrieve records, Query Records is the correct action.
Agentforce Specialist References
This information is confirmed from the Salesforce AI Specialist Material and Questions Document, where the Query Records action is explicitly defined as the appropriate standard action for retrieving filtered CRM records.
NEW QUESTION # 27
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?
Answer: C
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.
Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm
NEW QUESTION # 28
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC's Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should theAgentforce Specialisthighlight to UC that addresses these requirements?
Answer: C
Explanation:
Universal Containers (UC)has concerns about data privacy when usingSalesforce's generative AIfeatures, particularly around preventing third-party LLMs from accessing or retaining their data. TheZero-Data Retention Policyin theEinstein Trust Layeris designed to address these concerns by ensuring that:
* No data is used for trainingor product improvements by third-party LLMs.
* No data is retainedoutside of the customer's Salesforce organization.
* The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
* Prompt DefenseandData Maskingare also security features, but they do not directly address the concerns related to third-party data access and retention.
References:
* Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer.htm
NEW QUESTION # 29
An Agentforce 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 Agentforce Specialist gather the necessary data for the prompt template?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using aFlow(Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.
* Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don't natively support querying or aggregating related list data like the three most recent opportunities.
* Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don't directly query related objects without additional logic (e.g., a Flow), making this incorrect.
* Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.
Thus, Option B is the correct method to gather the necessary data efficiently and accurately.
References:
* Salesforce Agentforce Documentation: "Integrate Flows with Prompt Templates" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_flow_prompt_integration.htm&type=5)
* Trailhead: "Build Flows for Agentforce"(https://trailhead.salesforce.com/content/learn/modules/flows- for-agentforce)
NEW QUESTION # 30
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