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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Trust Layer & Security | - Data Masking
| |
| Topic 2: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 3: Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Topic 4: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
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NEW QUESTION # 262
What is the correct process to leverage Prompt Builder in a Salesforce org?
Answer: A
Explanation:
When using Prompt Builder in a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template type based on the use case.
Develop the prompt within the prompt workspace, where the template is created and customized.
Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.
Pick the model to use for generating responses, either using Salesforce's built-in models or custom ones.
Test and validate the generated responses to ensure accuracy and effectiveness.
NEW QUESTION # 263
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers. Which solution approach should the admin leverage?
Answer: B
Explanation:
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
NEW QUESTION # 264
Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer service operations. UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources. The team needs to configure the system to include all supported objects for grounding. Which objects should UC select to configure Service AI Grounding?
Answer: C
Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
Case
Knowledge
Case Object:
Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.
Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the specific customer issue being addressed.
Knowledge Object:
Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.
Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on verified information.
Exclusion of Other Objects:
Case Notes and Case Emails:
Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
NEW QUESTION # 265
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language. Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?
Answer: C
Explanation:
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the Agentforce Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.
NEW QUESTION # 266
An Agentforce implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce.
The Agentforce Specialist needs to configure the system to use the most accurate and up-to-date information for email generation. Which grounding technique should the Agentforce Specialist use?
Answer: C
Explanation:
For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.
Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.
NEW QUESTION # 267
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