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| Section | Weight | Objectives |
|---|---|---|
| Development Lifecycle | 15% | - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Prompt management and user roles - Grounding techniques and best practices |
| AI Agents | 35% | - Agent User security and permissions - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Agent architecture and reasoning engine - Topics and actions configuration |
| Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types |
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NEW QUESTION # 206
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 with Agents?
Answer: A
Explanation:
Universal Containers can achieve the request to explore similar opportunities by using the standard Copilot action. Agent has 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.
NEW QUESTION # 207
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI- driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?
Answer: A
Explanation:
UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last
6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.
NEW QUESTION # 208
When a customer chat is initiated, which functionality in Salesforce provides generative AI replies or draft emails based on recommended Knowledge articles?
Answer: B
Explanation:
When a customer chat is initiated, Einstein Service Replies provides generative AI replies or draft emails based on recommended Knowledge articles. This feature uses the information from the Salesforce Knowledge base to generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions.
NEW QUESTION # 209
A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent 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 Agent handle user requests In Salesforce?
Answer: B
Explanation:
Agent 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, Agent 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.
NEW QUESTION # 210
Universal Containers (UC) configured a new PDF file ingestion in Data Cloud with all the required fields, and also created the mapping and the search Index. UC Is now setting up the retriever and notices a required fleld is missing. How should UC resolve this?
Answer: B
Explanation:
Why is "Update the search index to include the desired field" the correct answer?
When configuring a retriever in Data Cloud for PDF file ingestion, all necessary fields must be included in the search index. If a required field is missing, the correct action is to update the search index to ensure it is available for retrieval.
Key Considerations for Fixing Missing Fields in Data Cloud Retrievers:
Search Index Controls Which Fields Are Searchable
The search index defines which fields are indexed and accessible to the retriever.
If a field is missing, it must be added to the index before it can be queried.
Ensures Complete and Accurate Data Retrieval
Without indexing, the retriever cannot reference the missing field in AI responses.
Updating the index makes the field available for AI-powered retrieval.
Supports AI-Grounded Responses
Agentforce relies on Retriever-Augmented Generation (RAG) to ground AI responses in searchable Data Cloud content.
Ensuring all relevant fields are indexed improves AI-generated answer accuracy.
NEW QUESTION # 211
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