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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 20% | - Grounding techniques and context management - Prompt Builder and template creation - Prompt best practices and optimization |
| Agent Development Lifecycle | 15% | - Testing and debugging in Testing Center - Monitoring, adoption and governance - Sandbox to production deployment |
| AI Agents and Agentforce Concepts | 35% | - Topics, actions, and reasoning engine - Agent types: Employee, Service, Sales - Agent security and user management - Channel integration and deployment |
| Multi-Agent Interoperability | 10% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage and integration |
| Data Cloud for Agentforce | 20% | - Data Library and unstructured data processing - Vector, keyword and hybrid search - Chunking, indexing and retrieval methods |
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NEW QUESTION # 190
Choose 1 option.
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.
Option A, Field Generation, is used to populate or update Salesforce fields, not for external invocation.
Option B, Sales Email, is specific to generating pre-formatted communication messages and cannot be invoked directly from LWCs.
Therefore, the correct template type for a prompt used within a Lightning Web Component is Option C - Flex, as it is purpose-built for dynamic, reusable, and programmatic use cases.
Reference: AgentForce Developer Guide - "Using Flex Prompt Templates with Lightning Web Components."
NEW QUESTION # 191
Choose 1 option.
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past.
What should UC consider when implementing this approach?
Answer: B
Explanation:
According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.
The documentation clearly explains:
"When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases." This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user's query.
Option A is incorrect because past cases cannot be used automatically without indexing them.
Option B is incorrect because a DMO is for structured data (tables, numeric fields) and doesn't support semantic text retrieval.
Therefore, Option C is correct and aligns fully with Salesforce's documented best practices.
References (AgentForce Documents / Study Guide):
* AgentForce Data Configuration Guide: "Using UDMOs for Case-Based Reasoning"
* AgentForce Implementation Handbook: "Indexing Historical Case Records for Semantic Search"
* AgentForce Study Guide: "Creating Unstructured Data Model Objects from Case Objects"
NEW QUESTION # 192
Once a data source is chosen for an Agentforce Data Library, what is true about changing that data source later?
Answer: B
Explanation:
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce's AI-driven environment.
Key Considerations in Agentforce Data Library
* Data Source Lock-In
* The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.
* Any updates or modifications require creating a new Data Library instead of modifying the existing one.
* Why Can't the Data Source Be Changed?
* The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.
* Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.
* Workarounds for Changing Data Sources
* If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.
* Old data can be manually migrated into the new data source for continuity.
Why Not the Other Options?
# A. The data source can be changed through the Data Cloud settings.
* Incorrect because once the data source is linked to an Agentforce Data Library, it cannot be altered, even via Data Cloud settings.
# B. The Data Retriever can be reconfigured to use a different data source.
* Incorrect as the Data Retriever works within the constraints of the selected data source and does not provide an option to swap data sources post-selection.
Agentforce Specialist References
The Salesforce AI Specialist Material and Salesforce Instructions for the Certification confirm that once a data source is set for an Agentforce Data Library, it cannot be changed.
NEW QUESTION # 193
An Agentforce configured Data Masking within the Einstein Trust Layer.
How should the Agentforce Specialist begin validating that the correct fields are being masked?
Answer: C
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the Agentforce Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu.
This audit data allows the Agentforce Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
NEW QUESTION # 194
During retrieval-augmented generation (RAG) quality testing, an Agentforce Specialist notices that tabular information from a custom Data 360 Document Ingestion Pipeline is losing its context because the data is scattered across multiple separate chunks.
What is the most appropriate approach to resolve this?
Answer: B
Explanation:
The correct answer is A. The problem is not that the retriever lacks enough sources; the problem is that the document parser is breaking structured tabular content into chunks that lose layout context.
Salesforce Data 360 includes parsing and preprocessing options for unstructured content, and the Docling parser is specifically intended for stronger layout understanding, including table-heavy or complex document structures. Option B is wrong because an ensemble retriever searches multiple retrievers or sources; it does not repair poor chunking caused by weak document parsing. Option C is also wrong because keyword-only scoring changes retrieval ranking behavior but does not preserve table relationships during ingestion. The correct fix is upstream: improve parsing before vectorization and indexing so the chunks preserve table context for RAG grounding.
NEW QUESTION # 195
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