QUESTION: 9

Universal Containers (UC) wants to implement an AI-powered customer service agent that can:

Retrieve proprietary policy documents that are stored as PDFs.

Ensure responses are grounded in approved company data, not generic LLM knowledge.

What should UC do first?

Set up an Agentforce Data Library for AI retrieval of policy documents.
Expand the AI agent's scope to search all Salesforce records.
Add the files to the content, and then select the data library option.

Answer(s): A

Explanation:

To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. The Agentforce Data Library (Option A) is the correct starting point. A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud's vector database, and make them available for AI retrieval.

This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC's requirements.

Option B: Expanding the AI agent's scope to search all Salesforce records is too broad and unnecessary at this stage. The requirement focuses on PDFs with policy documents, not all Salesforce data (e.g., cases, accounts), making this premature and irrelevant as a first step.

Option C: "Add the files to the content, and then select the data library option" is vague and not a precise process in Agentforce.
While uploading files is part of setting up a Data Library, the phrasing suggests adding files to Salesforce Content (e.g., ContentDocument) without indexing, which doesn't enable AI retrieval. Setting up the Data Library (A) encompasses the full process correctly.

Option A: This is the foundational step--creating a Data Library ensures the PDFs are uploaded, indexed, and retrievable by the agent, fulfilling both retrieval and grounding needs.

Option A is the correct first step for UC to achieve its goals.

Reference:

Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_data_library.htm&type=5)

Salesforce Data Cloud Documentation: "Ground AI Responses with Data Cloud" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_agentforce.htm&type=5)

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QUESTION: 10

A customer service representative is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related to this Itinerary. The representative needs to review the Knowledge articles about canceling and rebooking the customer flights.
Which Agentforce capability helps the representative accomplish this?

Invoke a flow which makes a call to external data to create a Knowledge article.
Execute tasks based on available actions, answering questions using information from accessible Knowledge articles.
Generate Knowledge article based off the prompts that the agent enters to create steps to cancel flights.

Answer(s): B

Explanation:

The scenario involves a customer service representative needing to cancel flights due to a weather alert and review existing Knowledge articles for guidance on canceling and rebooking. Agentforce provides capabilities to streamline such tasks. The most suitable option is Option B, which allows the agent to "execute tasks based on available actions" (e.g., canceling flights via a predefined action) while "answering questions using information from accessible Knowledge articles." This capability leverages Agentforce's ability to integrate Knowledge articles into the agent's responses, enabling the representative to ask questions (e.g., "How do I cancel a flight?") and receive AI-generated answers grounded in approved Knowledge content. Simultaneously, the agent can trigger actions (e.g., a Flow to update the custom object) to perform the cancellations, meeting all requirements efficiently.

Option A: Invoking a Flow to call external data and create a Knowledge article is unnecessary. The representative needs to review existing articles, not create new ones, and there's no indication external data is required for this task.

Option B: This is correct. It combines task execution (canceling flights) with Knowledge article retrieval, aligning with the representative's need to act and seek guidance from existing content.

Option C: Generating a new Knowledge article based on prompts is not relevant. The representative needs to use existing articles, not author new ones, especially in a time-sensitive weather alert scenario.

Option B best supports the representative's workflow in Agentforce.

Reference:

Salesforce Agentforce Documentation: "Knowledge Replies and Actions" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5)

Trailhead: "Agentforce for Service"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)

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QUESTION: 11

Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Agentforce for Service features enables this effort?

Einstein Reply Recommendations and Case Classification
Einstein Reply Recommendations and Case Summaries
Einstein Service Replies and Work Summaries

Answer(s): B

Explanation:

Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in- chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.

Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.

Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.

Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.

Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.

Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).

Thus, Option A is the correct answer for UC's needs.

Reference:

Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)

Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.case_classification.htm&type=5)

Trailhead: "Agentforce for Service"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)

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QUESTION: 12

Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses. UC notices that the retriever is returning too many irrelevant results, making the responses less useful.
What should UC do to ensure only relevant data is retrieved?

Define filters to narrow the search results based on specific conditions.
Change the search index to a different data model object (DMO).
Increase the maximum number of results returned to capture a broader dataset.

Answer(s): A

Explanation:

In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the `Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.

Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.

Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.

Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.

Option A is the most effective step to ensure relevance in retrieved data.

Reference:

Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)

Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)

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