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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt management and user roles |
| Topic 2: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
| Topic 3: Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Improving response accuracy with data grounding |
| Topic 4: AI Agents | 35% | - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent User security and permissions - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine |
| Topic 5: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
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NEW QUESTION # 271
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated. How should the Agentforce Specialist verify the privacy of the masked data in the Einstein Trust Layer?
Answer: B
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.
NEW QUESTION # 272
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?
Answer: C
Explanation:
According to the AgentForce Knowledge Integration and Access Configuration Guide, a Service Agent retrieves and grounds its responses using data from Salesforce Knowledge when the correct permissions are assigned. If customers report that the agent cannot access or provide answers from Knowledge articles, the most common root cause is that the AgentForce Service Agent user lacks the "Allow View Knowledge" permission.
This permission enables the agent to retrieve and read published articles from Salesforce Knowledge for grounding responses. Without it, the agent cannot access the content repository, resulting in incomplete or generic answers.
NEW QUESTION # 273
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier.
They need those preferences to persist throughout the session. What should CCR implement?
Answer: B
Explanation:
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters."
NEW QUESTION # 274
Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog. Which type of prompt template should UC use?
Answer: C
Explanation:
Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is a Flex prompt template.
A Flex prompt template is designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.
NEW QUESTION # 275
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level. What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?
Answer: A
Explanation:
In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1 indicates that the content is deemed safe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.
For further reference, check Salesforce's official Einstein Trust Layer documentation regarding toxicity scoring for AI-generated content.
NEW QUESTION # 276
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