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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid |
| Topic 2: AI Agents | 35% | - Deterministic controls: filters and variables - Agent User security and permissions - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent types: Service, Sales, Employee |
| Topic 3: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 4: Prompt Engineering | 20% | - Prompt management and user roles - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Grounding techniques and best practices |
| Topic 5: Development Lifecycle | 15% | - Version control and maintenance - Deployment: sandbox to production - Monitoring, adoption and optimization - Testing agents in Testing Center |
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NEW QUESTION # 101
Universal Containers (UC) needs to create a prompt template that provides a detailed product description based on the latest product data. The description will be used in marketing materials to ensure consistency and accuracy. Which prompt template type should UC use?
Answer: B
NEW QUESTION # 102
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
Answer: B
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 # 103
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?
Answer: B
Explanation:
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses.
NEW QUESTION # 104
An Agentforce wants to use the related lists from an account in a custom prompt template. What should the Agentforce Specialist consider when configuring the prompt template?
Answer: A
Explanation:
When configuring a custom prompt template to use related lists, the Agentforce Specialist must be aware of the maximum number of related list merge fields that can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.
NEW QUESTION # 105
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
- No data is used for LLM training or product improvements by third-
party LLMs.
- No data is retained outside of UC's Salesforce org.
- The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the Agentforce Specialist highlight to UC that addresses these requirements?
Answer: B
Explanation:
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data.
The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside of the customer's Salesforce organization.
The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
NEW QUESTION # 106
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