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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Topic 2: Prompt Engineering | 20% | - Grounding techniques
|
| Topic 3: Governance & Observability | 10% | - AI governance
|
| Topic 4: Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Topic 5: AI Agents | 35% | - Agent setup and orchestration
|
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NEW QUESTION # 140
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management. For security purposes, UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed. Which approach best meets this requirement?
Answer: B
Explanation:
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable- based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example, `isVerified') that stores the result of an identity verification step.
Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals `true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.
NEW QUESTION # 141
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
Answer: A
Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.
NEW QUESTION # 142
An Agentforce is setting up a new org and needs to ensure that users can create and execute prompt templates. The Agentforce Specialist is unsure which roles are necessary for these tasks.
Which permission sets should the Agentforce Specialist assign to users who need to create and execute prompt templates?
Answer: A
Explanation:
To effectively manage and use prompt templates, two distinct permission sets are required:
Prompt Template Manager: This permission set allows users to create prompt templates. It provides the necessary access to define templates, which can be shared and utilized across the organization.
Prompt Template User: This permission set is designed for users who need to execute the templates. It provides the ability to interact with pre-designed prompts and generate outcomes based on these templates.
The Data Cloud Admin permission set is not directly relevant to creating or executing prompt templates but is more focused on managing the Data Cloud.
NEW QUESTION # 143
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?
Answer: B
Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses.
Key considerations:
NEW QUESTION # 144
An Agentforce is creating a custom action for Agentforce. Which setting should the a ensure the action performs as expected?
Answer: B
Explanation:
When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.
Salesforce documentation emphasizes that Action Instructions directly impact the bot's ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot's behavior with business requirements.
NEW QUESTION # 145
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