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| Section | Weight | Objectives |
|---|---|---|
| Development Lifecycle | 15% | - Deployment: sandbox to production - Version control and maintenance - Testing agents in Testing Center - Monitoring, adoption and optimization |
| AI Agents | 35% | - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent User security and permissions - Agent architecture and reasoning engine |
| 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 |
| Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Model Context Protocol (MCP) - Agent-to-agent communication |
| Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types |
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NEW QUESTION # 56
During configuration, Universal Containers (UC) forgot to grant Knowledge access to the Agentforce Service Agent. Which permission must UC add for the agent to interact with Knowledge articles and answer customer questions effectively?
Answer: A
Explanation:
According to the AgentForce for Service Configuration Guide, for an agent to interact with and retrieve Knowledge articles, it must have Knowledge object and field access, along with the
"Allow View Knowledge" permission. The documentation explains: "Agents need permission to access Knowledge records and their fields to retrieve and summarize content accurately.
Additionally, the Allow View Knowledge setting enables the agent to use the Knowledge object as a retrieval source."
NEW QUESTION # 57
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?
Answer: C
Explanation:
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
NEW QUESTION # 58
Universal Containers (UC) needs to create a prompt template that provides a detailed product description based on the latest product data. The description will be used in marketing materials to ensure consistency and accuracy. Which prompt template type should UC use?
Answer: A
NEW QUESTION # 59
Universal Containers implemented Agentforce for its users. One user complains that an Agent is not deleting activities from the past 7 days. What is the reason for this issue?
Answer: B
Explanation:
Universal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user reports that an Agent is unable to delete recent activities.
Why Agentforce Cannot Delete Records
Agentforce's Standard Actions: Agentforce typically has predefined or "standard" actions like Create, Update, or Summarize records. However, a standard Delete Record action is not part of the default set of Agentforce actions.
Implication: If Agentforce has no built-in delete functionality, it cannot remove activities-even if the user has permission to delete them in the Salesforce UI.
NEW QUESTION # 60
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?
Answer: A
Explanation:
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
NEW QUESTION # 61
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