Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?
Correct Answer:B
Service AI Grounding retrieves data from Salesforce objects to ground AI- generated responses. Key considerations:
✑ Field Types: Standard Service AI Grounding supports String and Text Area fields.
Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
✑ Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
✑ Visibility: Service AI Grounding respects user permissions and sharing settings
unless overridden (Option C is incorrect).
References:
✑ Salesforce Help: Service AI Grounding Requirements
✑ Explicitly states support for "Text Area and String fields" only.
Universal Containers (UC) plans to implement prompt templates that utilize the standard foundation models. What should UC consider when building prompt templates in Prompt Builder?
Correct Answer:B
Comprehensive and Detailed In-Depth Explanation:UC is using Prompt Builder with standard foundation models (e.g., via Atlas Reasoning Engine). Let??s assess best practices for prompt design.
✑ Option A: Include multiple-choice questions within the prompt to test the LLM??s understanding of the context.Prompt templates are designed to generate responses, not to test the LLM with multiple-choice questions. This approach is impractical and not supported by Prompt Builder??s purpose, making it incorrect.
✑ Option B: Ask it to role-play as a character in the prompt template to provide more context to the LLM.A key consideration in Prompt Builder is crafting clear, context- rich prompts. Instructing the LLM to adopt a role (e.g., ??Act as a sales expert??) enhances context and tailors responses to UC??s needs, especially with standard models. This is a documented best practice for improving output relevance, making it the correct answer.
✑ Option C: Train LLM with data using different writing styles including word choice,
intensifiers, emojis, and punctuation.Standard foundation models in Agentforce are pretrained and not user-trainable. Prompt Builder users refine prompts, not the LLM itself, making this incorrect.
Why Option B is Correct:Role-playing enhances context for standard models, a recommended technique in Prompt Builder for effective outputs, as per Salesforce guidelines.
References:
✑ Salesforce Agentforce Documentation: Prompt Builder > Best Practices – Recommends role-based context.
✑ Trailhead: Build Prompt Templates in Agentforce – Highlights role-playing for clarity.
✑ Salesforce Help: Prompt Design Tips – Suggests contextual roles.
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?
Correct Answer:C
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
✑ Agentforce Role-Based Access Control
✑ Fixing the Issue
Why Not the Other Options?
* A. Sales rep users' profiles are missing the Allow SDR Agent permission.
✑ Incorrect because "Allow SDR Agent" is not a standard permission setting in Agentforce.
✑ Permission is granted via permission sets, not profile-level settings.
* B. Sales rep users do not have access to the SDR Agent object.
✑ Incorrect because there is no separate "SDR Agent object" in Salesforce.
✑ SDR Agents are AI-driven features, not standard CRM objects that require object- level access.
Agentforce Specialist References
✑ Salesforce AI Specialist Material confirms that users require specific permission sets to access Agentforce SDR Agents.
✑ Salesforce Instructions for Certification highlight the role of permission sets in controlling Agentforce access.
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should An Agentforce apply the power of conversational AI to this use case?
Correct Answer:B
To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Agent to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
✑ Option B is correct because it enables integration between Agent and the flow that
connects to Oracle ERP.
✑ Option A (Flex prompt template) is more suited for static responses and not for invoking flows.
✑ Option C (Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.
References:
✑ Salesforce Agent Actions: https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm
Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments. Which Einstein Generative AI feature should An Agentforce recommend for this request?
Correct Answer:A
Einstein Call Summaries is the best option for this scenario because it leverages Salesforce's AI capabilities to automatically summarize key details of video or voice calls. It includes details like deal terms, customer sentiments, follow-up tasks, and other crucial information. This feature is designed to help sales teams focus on their strategies rather than taking extensive manual notes during conversations.
✑ Einstein Call Summaries: Automatically generates summaries for calls, identifying
critical points such as next steps and follow-ups, enhancing efficiency and
understanding of deal progression.
✑ Einstein Conversation Insights: While it provides insights into customer sentiment and engagement, it is more suited for analyzing patterns across conversations rather than summarizing specific call details.
✑ Einstein Video KPI: Focuses on analyzing key performance indicators within video calls but does not offer summarization features needed for deal terms or sentiment tracking.
This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.
Reference:
"Boost Sales with Automated AI Strategies | Salesforce Trailhead" . "Introduction to Einstein Discovery | Salesforce" .