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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 2: Agent Force Concepts | 30% | - Einstein GPT Integration
|
| Topic 3: Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Topic 4: Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
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NEW QUESTION # 106
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
Answer: C
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:
Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
NEW QUESTION # 107
An Agentforce Specialist is creating a custom agent action. The topic is selected correctly, but the action is not. Which setting should the Agentforce Specialist test and iterate on to ensure the action performs as expected?
Answer: C
Explanation:
Per the AgentForce Custom Action Development Guide, if a topic is correctly triggered but the wrong action is executed, the issue typically lies in action instructions. The documentation notes:
"Action instructions provide the LLM with explicit guidance on when and how to use a given action. Poorly written or ambiguous instructions can cause the reasoning engine to select an incorrect action, even within the right topic."
NEW QUESTION # 108
Cloud Kicks (CK) recently finished the development of a new prompt template that uses its own large language model (LLM). CK is deploying a prompt template from a sandbox to a production org, and is receiving an error. When trying to deploy the change set, CK is getting an error related to the LLM used in the prompt template. What is the cause of the error?
Answer: C
Explanation:
As documented in the AgentForce Prompt Template and BYOLLM (Bring-Your-Own-LLM) Deployment Guide, each prompt template references a specific LLM configuration by name and ID. When migrating components between environments (e.g., from sandbox to production), the referenced LLM must also exist in the target org with the exact same name and identifier.
If the LLM configuration is missing or named differently in production, the deployment fails, as the prompt template cannot resolve its model dependency.
NEW QUESTION # 109
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio. Which use case works for this scenario?
Answer: C
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
NEW QUESTION # 110
How does the AI Retriever function within Data Cloud?
Answer: A
Explanation:
The AI Retriever is a key component in Salesforce Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let's evaluate each option based on its documented functionality.
NEW QUESTION # 111
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