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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data and AI Governance | 20% | - Ethics and compliance
|
| Topic 2: Prompt Engineering | 25% | - Prompt Builder
|
| Topic 3: AI Agents | 35% | - Security and governance
|
| Topic 4: Testing, Deployment, and Operations | 20% | - Lifecycle management
|
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NEW QUESTION # 208
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
Answer: A
Explanation:
The most critical technical consideration when pairing a prompt template's grounding data with a chosen Large Language Model (LLM) is the relationship between the two. The correct action is to review the model limitation in Prompt Builder versus the grounding data size (C).
Every LLM has a fixed context window limit, typically expressed in tokens (the model's units for processing text). This token limit defines the maximum amount of input data (the prompt template text + all the dynamic grounding data) and output data the model can handle in a single request.
The grounding data, which is pulled dynamically from Salesforce records (e.g., related lists, long text fields, Flow outputs), varies significantly in size from one record to the next. If the combined size of the prompt and the dynamic data for a specific record exceeds the LLM's token limit, the generative AI request will fail with a "token limit exceeded" error. The Agentforce Specialist must proactively design the template to limit the amount of data retrieved (e.g., using Flow to summarize related lists or querying only essential fields) to ensure it stays within the chosen model's capacity.
Option A is incorrect because the Einstein Trust Layer's token limit primarily relates to PII masking and is a security-related capacity, not the fundamental model's context window. Option B is incorrect because OFFSET is a SOQL query function used for pagination, which is irrelevant to ensuring the total size of the final assembled prompt (template + data) fits within the model's token limit.
Simulated Exact Extract of AgentForce documents (Conceptual Reference):
"A major challenge in prompt template design is managing the Large Language Model (LLM) token limit against the volume of grounding data. The specialist must always Review the model limitation in Prompt Builder versus the grounding data size before activation. LLM context windows (token limits) are fixed per model, but dynamic prompt components-such as merge fields from related lists or long text area fields-can cause the total size of the prompt to vary significantly by record. To prevent random token limit failures, the prompt instructions and grounding logic (Flow/Apex) must be explicitly constrained to retrieve only the essential data required to answer the query, ensuring the combined input stays well below the LLM's defined capacity." Simulated Reference: AgentForce Prompt Builder Best Practices Guide, Section 4: Performance and Scalability, p. 92.
NEW QUESTION # 209
Before activating a custom copilot action, An Agentforce would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.
Which tool should the Agentforce Specialist recommend?
Answer: A
Explanation:
Model Playground(specifically within the context of Generative AI and Copilot in Salesforce) allows you to test and refine the behavior of your AI models and, by extension, how your copilot actions interpret and respond to different user inputs (utterances). It's a sandbox environment where you can:
* Input various user utterances.
* See how the underlying Large Language Model (LLM) and the copilot's reasoning engine classify those utterances.
* Observe which actions are triggered by those utterances.
* Adjust the action instructions and examples to improve the copilot's understanding and ensure the correct action is chosen for specific user requests. This iterative testing is crucial for ensuring the action performs as expected in real-world scenarios.
NEW QUESTION # 210
Universal Containers is deploying two agents simultaneously: an internal Sales Productivity Agent for employees and a customer-facing Service Agent on its Experience Cloud site. An Agentforce Specialist is configuring permissions and needs to understand the correct security context for each.
Which statement accurately describes the execution model?
Answer: C
Explanation:
The correct answer is B. Internal employee-facing agents operate within the authenticated Salesforce user's access model, so actions must respect the employee's profile, permission sets, sharing, and field- level security. A customer-facing Service Agent deployed through Experience Cloud is different: it is configured around an agent user/service agent execution identity, and Salesforce guidance stresses assigning the correct permissions to that agent user rather than relying on the site guest profile. Option A is wrong because a logged-in Experience Cloud deployment should not be treated as a guest-user execution model by default. Option C is also wrong because it incorrectly applies the dedicated Service Agent User model to the internal Sales Productivity Agent. The exam distinction is user-context execution for internal productivity versus dedicated service-agent execution for customer-facing service automation.
NEW QUESTION # 211
An Agentforce at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the Agentforce Specialist saves, tests, and activates it. Howsoever, when they go to a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the Agentforce Specialist was editing the field, it was behaving as a normal field.
Which critical step did the Agentforce Specialist miss?
Answer: B
Explanation:
For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the template will result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.
* Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.
* Case objects are supported for Field Generation (B is incorrect).
Reference:
Salesforce Help Article: Configure Field Generation Prompt Templates ("Associating Fields with Page Layouts" section).
Einstein GPT Implementation Guide: "Enabling AI-Generated Fields in Lightning Pages."
NEW QUESTION # 212
Choose 1 option.
Cloud Kicks wants to integrate its agent with its custom website. The goal is for customers to interact with the custom agent chat interface.
Which approach provides the framework for the custom web application to communicate with the agent?
Answer: C
Explanation:
The AgentForce API Integration Guide defines the Agent API as the framework that enables external web or mobile applications to communicate directly with Salesforce-hosted agents. This API supports message exchange, session management, and context persistence - allowing developers to build custom chat interfaces while maintaining secure, real-time connectivity with the AgentForce reasoning engine.
Option A (A2A) is for inter-agent collaboration within Salesforce, not for external web integration. Option B (MCP) - Model Context Protocol - is used for context sharing between models and tools, not for front- end integration.
Therefore, the correct framework for enabling communication between a custom website chat interface and an AgentForce agent is Option C - Agent API, as it provides the structured interface for external client applications.
Reference: AgentForce Integration Manual - "Using the Agent API for Web and Application-Based Interactions."
NEW QUESTION # 213
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