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| Section | Weight | Objectives |
|---|---|---|
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Agent Force Concepts | 30% | - Einstein GPT Integration
|
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NEW QUESTION # 196
An Agentforce has created a copilot custom action using flow as the reference action type.
However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting. What should the Agentforce Specialist do to identify the root cause of the problem?
Answer: A
Explanation:
When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder's Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the Agentforce Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.
NEW QUESTION # 197
Universal Containers has implemented an agent that answers questions based on Knowledge articles. Which topic and Agent Action will be shown in the Agent Builder?
Answer: C
Explanation:
UC's agent answers questions using Knowledge articles, configured in Agent Builder.
NEW QUESTION # 198
Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. The goal is to enhance the team's performance by identifying areas for improvement and competitive intelligence. Which feature provides insights about competitor mentions and coaching opportunities?
Answer: A
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
NEW QUESTION # 199
An Agentforce turned on Einstein Generative AI in Setup. Now, the Agentforce Specialist would like to create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in the Setup menu. What is causing the problem?
Answer: A
Explanation:
In order to access and create custom prompt templates in Prompt Builder, the Agentforce Specialist must have the Prompt Template Manager permission set assigned. Without this permission, they will not be able to access Prompt Builder in the Setup menu, even though Einstein Generative AI is enabled.
NEW QUESTION # 200
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts. Which approach should the Agentforce Specialist recommend to achieve this use case?
Answer: B
Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
NEW QUESTION # 201
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