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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Topic 2: Development Lifecycle | 15% | - Version control and maintenance - Testing agents in Testing Center - Monitoring, adoption and optimization - Deployment: sandbox to production |
| Topic 3: Prompt Engineering | 20% | - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt management and user roles |
| Topic 4: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Unstructured data processing: chunking, indexing |
| Topic 5: AI Agents | 35% | - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Topics and actions configuration - Agent User security and permissions |
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NEW QUESTION # 96
Universal Containers wants to implement a customer verification process where sensitive account information can only be accessed after the customer passes identity verification. The agent must enforce this security rule deterministically without allowing the large language model (LLM) to bypass the verification requirement. What should an Agentforce Specialist recommend as the best solution?
Answer: B
Explanation:
The AgentForce Security and Deterministic Logic Guide specifies that sensitive actions must be gated through conditional filters linked to verification variables, not through natural language. It states: "For any process requiring secure, deterministic access, create a custom variable (e.g., IsCustomerVerified) that stores the verification status as a Boolean. Apply a filter expression to all protected actions (e.g., IsCustomerVerified = true). This ensures the LLM cannot bypass or alter access logic." This configuration ensures security and determinism because the execution of sensitive actions is programmatically enforced, not dependent on the LLM's understanding.
NEW QUESTION # 97
Universal Containers needs its sales reps to be able to only execute prompt templates. What should the company use to achieve this requirement?
Answer: A
Explanation:
Salesforce Agentforce leverages Prompt Builder, a powerful tool that allows administrators to create and manage prompt templates, which are reusable frameworks for generating AI-driven responses. These templates can be invoked by users to perform specific tasks, such as generating sales emails or summarizing records, based on predefined instructions and grounded data. In this scenario, Universal Containers wants its sales reps to have the ability to only execute these prompt templates, meaning they should be able to run them but not create, edit, or manage them.
NEW QUESTION # 98
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature. What does Einstein consider when generating a summary?
Answer: A
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
NEW QUESTION # 99
Universal Containers wants to use an existing prompt template inside Flow as part of automation.
Answer: B
NEW QUESTION # 100
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS). What should the team use to access externally-hosted models in the Salesforce Platform?
Answer: A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
NEW QUESTION # 101
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