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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent setup and orchestration
|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Governance & Observability | 10% | - AI governance
|
| Prompt Engineering | 20% | - Prompt Builder usage
|
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NEW QUESTION # 118
Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page. After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?
Answer: B
Explanation:
For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.
NEW QUESTION # 119
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?
Answer: C
Explanation:
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.
NEW QUESTION # 120
A support manager wants an agent to summarize cases but prevent it from exposing customer PII. Which feature directly addresses this requirement?
Answer: A
Explanation:
The Einstein Trust Layer can mask or exclude sensitive fields, preventing PII from being exposed in AI responses.
NEW QUESTION # 121
What is the primary function of the reasoning engine in Agentforce?
Answer: A
Explanation:
Why is "Identifying agent topics and actions to respond to user utterances" the correct answer?
In Agentforce, the reasoning engine plays a critical role in interpreting user queries and determining the appropriate agent response.
Key Functions of the Reasoning Engine in Agentforce:
Analyzing User Intent
The reasoning engine interprets the meaning behind natural language user inputs.
It maps user utterances to predefined topics to determine the correct AI-generated response.
Selecting the Appropriate Agent Action
The engine evaluates available actions and selects the best response based on the detected topic.
For example, if a user asks, "What is my current account balance?", the reasoning engine:
Identifies the topic: "Account Information"
Chooses the correct action: "Retrieve account balance"
Executes the action and returns the response
Ensuring AI Accuracy and Context Awareness
The reasoning engine grounds AI-generated responses in relevant Salesforce data, ensuring accurate outputs.
NEW QUESTION # 122
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past. What should UC consider when implementing this approach?
Answer: B
Explanation:
According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.
The documentation clearly explains:
"When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases." This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user's query.
NEW QUESTION # 123
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