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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: AI Agents | 35% | - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent User security and permissions - Deterministic controls: filters and variables - Agent architecture and reasoning engine |
| Topic 3: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
| Topic 4: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 5: Development Lifecycle | 15% | - Deployment: sandbox to production - Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance |
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NEW QUESTION # 176
Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles.
When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?
Answer: B
Explanation:
UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud.
NEW QUESTION # 177
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization. What should the Agentforce Specialist recommend?
Answer: A
Explanation:
To measure adoption and usability of Agents across the organization, Agentforce Analytics is the right tool. It provides dashboards and reports that track how Agents are being used, which actions are triggered most often, and overall performance trends. This data helps organizations drive adoption by identifying gaps, monitoring usage, and demonstrating business value.
NEW QUESTION # 178
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?
Answer: A
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
NEW QUESTION # 179
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
Answer: C
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data.
The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI- powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.
NEW QUESTION # 180
Universal Containers deployed the new Agentforce Sales Development Representative (SDR) Into production, but sales reps are saying they can't find it. What is causing this issue?
Answer: A
Explanation:
Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?
If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.
Key Considerations for This Issue:
Permission Set Restriction
Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.
If they lack this permission, the SDR Agent will not appear in their interface.
Agentforce Role-Based Access Control
Agentforce assigns specific permissions based on user roles.
Sales reps require explicit permission to access the SDR Agent.
Fixing the Issue
The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales reps.
This is done in Setup Permission Sets Assign to Users.
NEW QUESTION # 181
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