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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Topic 3: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
| Topic 4: Development Lifecycle | 15% | - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance - Monitoring, adoption and optimization |
| Topic 5: AI Agents | 35% | - Deterministic controls: filters and variables - Topics and actions configuration - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Agent User security and permissions - Agent types: Service, Sales, Employee |
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NEW QUESTION # 207
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?
Answer: B
Explanation:
Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.
The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.
Einstein Prompt Builder Typical Use Cases
Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.
NEW QUESTION # 208
Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles.
On which AI capability should UC train the service agents?
Answer: C
Explanation:
Service Replies (specifically Einstein Service Replies) is the Salesforce Generative AI functionality designed to automatically draft responses for service agents in real-time, based on contextual information, including existing knowledge articles. This directly addresses Universal Containers' need to reduce handling time by providing recommended replies grounded in their knowledge base
NEW QUESTION # 209
How does Agentforce select the correct action to resolve a user's request?
Answer: C
Explanation:
In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states:
"AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context."
NEW QUESTION # 210
An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM data. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt.
Which feature in the Einstein Trust Layer best supports UC's need?
Answer: C
Explanation:
Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model's outputs are trustworthy and reliable for business use.
The other options are less aligned with the requirement:
Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.
NEW QUESTION # 211
Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates. Which type of flow should UC use?
Answer: C
Explanation:
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.
NEW QUESTION # 212
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