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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Agents | 35% | - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent User security and permissions - Topics and actions configuration - Agent types: Service, Sales, Employee |
| Topic 2: Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases |
| Topic 3: Development Lifecycle | 15% | - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production |
| Topic 4: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Topic 5: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
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NEW QUESTION # 97
Universal Containers wants to use an existing prompt template inside Flow as part of automation.
Answer: A
NEW QUESTION # 98
Universal Containers plans to enhance its sales team's productivity using AI. Which specific requirement necessitates the use of Prompt Builder?
Answer: C
Explanation:
UC seeks an AI solution for sales productivity.
NEW QUESTION # 99
What is a Salesforce Agentforce Specialist able to configure in Data Masking within the Einstein Trust Layer?
Answer: C
Explanation:
In the Einstein Trust Layer, the Salesforce Agentforce Specialist can configure privacy data entities to be masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated when processed by AI models.
Data Masking Configuration:
The Agentforce Specialist defines which fields or data types (e.g., email, phone number, Social Security Number) should be masked. For example, masking the Email field in a prompt response to protect user privacy.
This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.
NEW QUESTION # 100
Universal Containers (UC) is preparing and defining success criteria for Agentforce Testing Center test cases. Which details should UC specify as the expected output to ensure the tests accurately reflect the agent's functionality?
Answer: C
Explanation:
According to the AgentForce Testing Center Reference Guide, each test case in the Testing Center should define a clear expected output to validate that the agent selects and executes the correct topic in response to a given user utterance.
The Expected Topic API Name acts as the validation reference -- it ensures that the reasoning engine correctly classifies the user's intent and routes the conversation to the appropriate topic.
This allows the test to confirm end-to-end functionality, from intent detection to action execution.
NEW QUESTION # 101
After an agent selects a topic, what is an important factor the reasoning engine uses to select the action?
Answer: A
Explanation:
The most crucial factor a reasoning engine uses to select an action after a topic is chosen is the priority given to each action (A). In advanced agent frameworks like AgentForce (simulated context), actions within a topic are typically not executed simply in an explicit, fixed order () unless there's no conditional logic. Instead, the reasoning engine evaluates all available actions and their associated pre-conditions (or triggers) and priorities. A priority score is often a numerical value assigned to an action that dictates its relative importance when multiple actions could potentially be executed simultaneously or when the agent must choose the 'best' action to address the current topic state. This prioritization ensures the agent handles the most critical or relevant tasks first, which is essential for efficient and goal-oriented behavior. The action's name and instructions () are descriptive for the developer but are not the primary selection criteria used by the runtime reasoning engine itself; it's the logic and priority that govern execution.
NEW QUESTION # 102
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