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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
| Topic 2: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid |
| Topic 3: Prompt Engineering | 20% | - Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Grounding techniques and best practices |
| Topic 4: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Topic 5: AI Agents | 35% | - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine - Agent User security and permissions - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack |
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NEW QUESTION # 199
Universal Containers wants to use an existing prompt template inside Flow as part of automation.
Answer: B
NEW QUESTION # 200
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else. How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?
Answer: C
Explanation:
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well-defined output instructions and examples. The documentation specifies:
"To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key-value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language."
NEW QUESTION # 201
A sales manager needs to contact leads at scale with hyper-relevant solutions and customized communications in the most efficient manner possible. Which Salesforce solution best suits this need?
Answer: C
Explanation:
The question specifies a sales manager's need to:
Contact leads at scale: Handle a large volume of leads simultaneously.
Hyper-relevant solutions: Deliver tailored solutions based on lead-specific data (e.g., CRM data, behavior).
Customized communications: Personalize outreach (e.g., emails, messages) for each lead.
Most efficient manner possible: Minimize manual effort and maximize automation.
This suggests a solution that leverages AI for personalization and automation for scale, ideally within the Salesforce ecosystem.
NEW QUESTION # 202
Before activating a custom agent action, an AgentForce Specialist would like to evaluate multiple real-world user utterances to ensure the action is being selected appropriately. Which tool should the AgentForce Specialist recommend?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
The AgentForce Testing Center is the recommended tool for validating and refining agent behavior before activation. According to the AgentForce Quality Assurance and Testing Framework, the Testing Center enables specialists to simulate and analyze how the reasoning engine classifies user utterances, selects topics, and triggers actions across multiple test cases.
This testing environment provides detailed diagnostics, showing classification confidence, prompt resolution paths, and grounding behavior. It allows for adjustments before production deployment to ensure accuracy and relevance in real-world scenarios.
NEW QUESTION # 203
An Agentforce Specialist wants to ensure their custom agent action performs as expected in conversations. What should the Agentforce Specialist focus on when creating action instructions?
Answer: B
Explanation:
The AgentForce Action Design Guide emphasizes that concise and clear action instructions are essential for predictable and reliable agent behavior. The documentation states: "Action instructions should be concise, clearly define the purpose of the action, and specify its expected inputs and outputs. Each action must be validated through testing in AgentForce Builder to confirm the LLM interprets and executes it as intended."
NEW QUESTION # 204
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