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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Agents | 35% | - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Topics and actions configuration - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent User security and permissions |
| Topic 2: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Version control and maintenance - Deployment: sandbox to production |
| Topic 3: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid |
| Topic 4: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 5: 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 |
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NEW QUESTION # 111
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities. How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
Answer: C
Explanation:
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.
NEW QUESTION # 112
An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?
Answer: A
Explanation:
The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions.
NEW QUESTION # 113
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language. Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?
Answer: B
Explanation:
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the Agentforce Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.
NEW QUESTION # 114
After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?
Answer: C
Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
Low Temperature (e.g., 0.2): More deterministic, consistent responses.
High Temperature (e.g., 1.0): More creative, varied responses.
NEW QUESTION # 115
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents' usability and the assignment of actions?
Answer: C
Explanation:
Monitoring the usability and action assignments of Agentforce Agents requires insights into how agents perform, how users interact with them, and how actions are executed within conversations. Salesforce provides Agent Analytics (Option C) as a built-in capability specifically designed for this purpose. Agent Analytics offers dashboards and reports that track metrics such as agent response times, user satisfaction, action invocation frequency, and success rates. This tool allows the Agentforce Specialist to assess usability (e.g., are agents meeting user needs?) and monitor action assignments (e.g., which actions are triggered and how often), providing actionable data to optimize the pilot program.
NEW QUESTION # 116
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