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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
| Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
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NEW QUESTION # 46
Universal Containers (UC) needs to create a custom prompt template that can be called from a Lightning web component. Which prompt template type should UC create?
Answer: C
Explanation:
The AgentForce Developer Integration Guide specifies that Flex prompt templates are the correct type for custom or embedded integrations, such as invoking a prompt from a Lightning Web Component (LWC).
Flex templates are designed for general-purpose use cases and can be called programmatically via Apex, Flow, or LWC APIs. They offer flexible input and output structures, allowing developers to integrate AgentForce reasoning into custom applications and UI components.
NEW QUESTION # 47
Sales reps at Universal Containers should not be able to create or edit prompt templates. Which permission set should an AgentForce Specialist assign to the sales reps?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Permissions and Role Management Guide, permission sets control the level of access users have to Prompt Templates - which define how the reasoning engine interprets and generates responses. AgentForce distinguishes between users who create or manage templates and those who only execute or use them during interactions.
The Prompt Execute User permission set is designed for users who can invoke and run prompts but cannot create, modify, or delete prompt templates. This role is intended for front-line users such as sales reps, service agents, or support staff who utilize preconfigured templates in daily workflows without altering them.
NEW QUESTION # 48
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
Answer: C
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.
NEW QUESTION # 49
An Agentforce Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities. How should the Agentforce Specialist gather the necessary data for the prompt template?
Answer: A
Explanation:
In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using a Flow (Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.
NEW QUESTION # 50
What is an Agentforce Specialist able to do when the `Enrich event logs with conversation data' setting in the Agentforce configuration is enabled?
Answer: A
Explanation:
The AgentForce Event and Logging Configuration Guide states that enabling "Enrich event logs with conversation data" allows administrators to capture session-level details, including both user inputs and agent responses. The documentation explains: "When this setting is enabled, conversation transcripts, user messages, and agent responses are appended to the event logs for improved visibility and troubleshooting."
NEW QUESTION # 51
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