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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 25% | - Prompt Builder
|
| Data and AI Governance | 20% | - Data grounding
|
| Testing, Deployment, and Operations | 20% | - Lifecycle management
|
| AI Agents | 35% | - Security and governance
|
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NEW QUESTION # 182
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should An Agentforce apply the power of conversational AI to this use case?
Answer: B
Explanation:
To enableUniversal Containersservice agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is tocreate a custom copilot action that calls the flow. This action will allowAgentto interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
* Option Bis correct because it enables integration betweenAgentand the flow that connects to Oracle ERP.
* Option A(Flex prompt template) is more suited for static responses and not for invoking flows.
* Option C(Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.
:
Salesforce Agent Actions:https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm
NEW QUESTION # 183
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history.
Why should the team use Agent as the solution?
Answer: A
Explanation:
Agent is designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team at Universal Containers, this is the perfect solution to create dynamic and relevant email content. By leveraging Agent, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.
* Option A is correct as Agent helps generate real-time, personalized content based on comprehensive data about the customer.
* Option B refers more to Einstein Analytics or
* Marketing Cloud Intelligence, and Option C deals with automation, which isn't the primary focus of Agent.
References:
* Salesforce Agent Overview: https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.
htm
NEW QUESTION # 184
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the Agentforce Specialist recommend to achieve this use case?
Answer: A
Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.
g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
NEW QUESTION # 185
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?
Answer: C
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.
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)
NEW QUESTION # 186
What is An Agentforce able to do when the "Enrich event logs with conversation data" setting in Agent is enabled?
Answer: B
Explanation:
When the"Enrich event logs with conversation data"setting is enabled inAgent, it allows An Agentforce or admin to view session data, including both theuser inputandcopilot responsesfrom interactions over the past
7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
* This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
* Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.
* Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.
Salesforce Agentforce Specialist References:
You can refer to this documentation for further insights:https://help.salesforce.com/s/articleView?id=sf.
einstein_copilot_event_logging.htm
NEW QUESTION # 187
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