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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Agents | 35% | - Deterministic controls: filters and variables - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Topics and actions configuration - Agent User security and permissions - Channel integration: digital experience, email, Slack |
| Topic 2: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
| Topic 3: Development Lifecycle | 15% | - Version control and maintenance - Testing agents in Testing Center - Monitoring, adoption and optimization - Deployment: sandbox to production |
| Topic 4: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Prompt management and user roles - Grounding techniques and best practices |
| Topic 5: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid |
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NEW QUESTION # 235
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment. Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
Answer: A
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision- making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
NEW QUESTION # 236
A service agent is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related with this itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking the customer flights. Which Agent capability helps the agent accomplish this?
Answer: B
Explanation:
In this scenario, the Agent capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Agent can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.
This feature leverages the agent's existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer's needs.
NEW QUESTION # 237
An AgentForce Specialist wants to troubleshoot an agent that is hallucinating weblinks. The agent has an action that uses a prompt template, which is using a knowledge retriever, to generate the output text that the agent will use. Which process is appropriate to find the root cause of the hallucination behavior?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Troubleshooting and Optimization Guide, hallucinations - instances where the agent fabricates details such as weblinks or data - often occur due to issues in prompt construction or retrieved content grounding. The recommended diagnostic process involves inspecting the prompt template instructions and reviewing the resolved prompt output, including the actual retrieved knowledge chunks.
By examining these areas, the AgentForce Specialist can determine whether the hallucinated content originates from ambiguous prompt phrasing, missing grounding variables, or irrelevant retrieval results. This approach ensures an evidence-based investigation directly linked to the agent's reasoning and generation steps.
NEW QUESTION # 238
Universal Containers (UC) is looking to enhance its operational efficiency. UC has recently adopted Salesforce and is considering implementing Agent to improve its processes. What is a key reason for implementing Agent?
Answer: B
Explanation:
The key reason for implementing Agent is its ability to streamline workflows and automate repetitive tasks. By leveraging AI, Agent can assist users in handling mundane, repetitive processes, such as automatically generating insights, completing actions, and guiding users through complex processes, all of which significantly improve operational efficiency.
NEW QUESTION # 239
Universal Containers' administrator has developed a new agent in a sandbox environment and now wants to deploy it to production. What should the administrator do to deploy an agent?
Answer: B
Explanation:
As per the AgentForce Deployment and Lifecycle Management Guide, AgentForce agents, including their topics, actions, and prompt templates, can be deployed from sandbox to production using Salesforce change sets.
Administrators should create an outbound change set in the sandbox environment that includes all relevant components (Agent definition, prompt templates, topic configurations, flows, and permissions) and then upload it to production for validation and deployment. This process ensures consistency, auditability, and proper dependency tracking between environments.
NEW QUESTION # 240
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