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Agentforce Specialist

Agentforce Specialist

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Agentforce Specialist

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Platform Administrator
Platform App Builder
Platform Foundations
Platform Developer
Platform Administrator II
Agentforce Sales Consultant
Agentforce Service Consultant
Platform Data Architect
Platform Development Lifecycle and Deployment Architect
Platform Identity and Access Management Architect
Platform Integration Architect
Platform Sharing and Visibility Architect
Heroku Architect
B2C Solution Architect
Experience Cloud Consultant
Agentforce Field Service and Operations Consultant
Agentforce Nonprofit Consultant
Data 360 Consultant
Omnistudio Consultant
CRM Analytics and Einstein Discovery Consultant
Platform User Experience Designer
Platform Strategy Designer
B2C Commerce Developer
JavaScript Developer
Omnistudio Developer
Platform Developer II
Marketing Cloud Engagement Administrator
Marketing Cloud Engagement Specialist
Marketing Cloud Engagement Consultant
Agentforce Sales Foundations
Business Analyst
Marketing Cloud Engagement Developer
Marketing Cloud Engagement Foundations
Agentforce Specialist
Agentforce Life Sciences Consultant
B2B Commerce Administrator AP
B2B Commerce Developer AP
Agentforce Consumer Goods AP
Agentforce Financial Services AP
Agentforce Health AP
Agentforce Manufacturing AP
MuleSoft Integration Foundations
MuleSoft Developer
MuleSoft Developer II
MuleSoft Platform Integration Architect
MuleSoft Platform Architect
Tableau Desktop Foundations
Tableau Data Analyst
Tableau Consultant
Tableau Server Administrator
Tableau Architect

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When to use Prompt Builder
Prompt Builder versus predictive AI
Prompt Builder versus Flow automation
Prompt Builder versus Agent Builder
Prompt templates as Agentforce actions
Prompt Builder enablement and user assignment
Prompt Template Manager versus Prompt Template User
Type-specific permissions for prompt templates
Running-user data access for prompt templates
Field Generation prompt templates
Flex prompt templates
Sales Email prompt templates
Record Summary prompt templates
CRM merge fields for prompt grounding
Flow versus merge fields for prompt grounding
Apex merge fields versus Flow for prompt grounding
Knowledge RAG grounding in prompt templates
Data 360 retriever grounding in Prompt Builder
Combining merge fields with retrieval grounding
Web grounding for real-time external data
File input grounding in Prompt Builder
Prompt template creation in Prompt Builder
Prompt template preview and revision
Prompt template activation and draft status
Prompt template execution surfaces
Prompt template anatomy and design
Merge fields versus hardcoded values in prompt text
Grounded-response instructions in prompt templates
Context engineering versus prompt engineering
Context clash, confusion, and poisoning
Einstein Trust Layer architecture
Secure data retrieval for prompt grounding
Pattern-based and field-based LLM data masking
Prompt defense system policies
Zero-data retention with third-party LLMs
Toxicity detection on generated responses
Generative AI audit trail and feedback data
Supported LLMs for Prompt Builder and Agentforce
Org-level model enablement and blocking
Prompt template model selection
Agentforce model option and subagent overrides

Explain the considerations of Agentforce Data Library and its concepts.

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Purpose of Agentforce Data Library
Data Library source types
Knowledge grounding with a Data Library
Data Library setup versus runtime
Data Library setup considerations
Assigning a Data Library to an agent
Default versus additional Data Libraries
Custom retrievers in a Data Library
Data Library versus advanced RAG setup

Explain foundational concepts of Data 360 such as chunking, indexing, and retrievers

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Retrieval augmented generation in Data 360

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Chunking sources for AI retrieval

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Search indexes for chunked Data 360 content

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Vector versus hybrid search indexes

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Retrievers versus search indexes

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Retriever creation and activation for grounding

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Ensemble retrievers in Data 360

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Knowledge retrieval troubleshooting for agents

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Agentforce agent building blocks
Atlas reasoning engine request handling
Subagents versus actions
Action chaining and subagent transitions
Agent Script as the Agentforce Builder language
Agent Script block types
Next-generation authoring of Agent Script
Hybrid reasoning components in Agent Script
Benefits of hybrid reasoning
Agent Script in Canvas View
Agent Script in Script View
Canvas versus Script View
Before and after reasoning in Agent Script
Topic filters for request gating
Action filters for business-rule gating
Input variables versus hardcoded action values
Output variables for action-to-action data
Context and conversation variables
Template expressions in agent instructions
Conditional logic for context-specific prompts
Standard versus custom topics
Topic classification descriptions and routing
Custom topic scope and instructions
Standard versus custom agent actions
Custom action types from Flow, Apex, and prompt templates
Creating custom agent actions
Agent action descriptions and instructions
Topic-level versus action-level instructions
Assigning actions to a topic
User access for standard agent actions
Supported channels for Employee and Service agents
Service Agent connections to digital experiences
Service Agent connections to messaging channels
Service Agent connections to email
Service Agent connections to Agentforce Voice
Omni-Channel routing to a Service Agent
Employee Agent connections to Slack
Employee Agent connections to Lightning Experience and Mobile
Execution identity by agent type
Running-user permissions and sharing for actions
Employee agent access for end users
Credential-based verification and Service run-as
Identity verification in sensitive agent actions
Context variables versus data access control
Employee versus Service agent selection
Agentforce Default versus Employee agent
When to use Agent API versus standard channels
Agent API supported agent types
Agent API versus Models API
Agent API versus custom agent actions
Agent API versus other Agentforce APIs and SDKs
Agent API actions in Flow and Apex

Agent testing strategy from preparation through retest
Agentforce Builder versus Testing Center testing
Builder Simulate versus Live Test modes
Permissions for Testing Center tests
Testing Center test case criteria
Test data and conversation context for Testing Center
Batch testing and iteration in Testing Center
AI-generated tests in Testing Center
Testing Center subagent and action evaluation
Testing Center response evaluation
Testing Center results interpretation
Quality metric scorers in Testing Center
Custom Testing Center scorers
Testing Center limits and considerations
Change sets versus Agentforce DX for agent deployment
Dependent components in an agent change set
Target-org prerequisites for a deployed agent
Agent activation after production deployment
Post-deployment channel and connection configuration
Agent versioning for uninterrupted production updates
Data 360 and Data Library considerations for agent promotion
Prompt template change sets and metadata deployment
Dependent metadata for prompt template deployment
Prompt Builder enablement in the production org
Prompt template activation in the destination org
Prompt template version behavior on deploy
Field generation Lightning page assignment after deploy
Retriever and Knowledge grounding after template deployment

Monitoring in the Agent Development Lifecycle
Agent manager permission sets
Activate and deactivate agents
Agent versioning lifecycle
Agentforce guardrails and trust patterns
AI agent risk identification
Agent guardrail monitoring
Einstein Audit and Feedback setup
Audit trail and toxicity review
Enhanced Event Logs for agent monitoring
Investigating agent sessions and intents
Omni-Channel monitoring for Service agents
Agent Analytics versus Agent Optimization
Agent Analytics metrics dashboards and session insights
Session Tracing Data Model for observability
Intents in Agent Optimization
Utterance analysis for agent conversations
Quality scores and subagent performance trends
Scorers and custom scorers in Agentforce Studio
Context engineering for agent optimization
Legacy Agentforce Analytics versus Agent Analytics

Single-agent versus Multi Agent architecture
Subagents versus a Multi Agent architecture
When to use Model Context Protocol (MCP)
When to use Agent-to-Agent (A2A) protocol

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Chunking sources for AI retrieval

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Practice Question

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Next conceptSearch indexes for chunked Data 360 content

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Explainer

Chunking splits large grounding sources into smaller pieces so retrieval can find the passages that match a question without sending an entire document into one LLM call. Data 360 then indexes those chunks as vector embeddings and scores them against the query. Only high-similarity chunks are added back into the prompt, which keeps responses grounded without exceeding the model’s token limit.

Core information

Grounding adds domain-specific or customer information to a prompt, but long knowledge articles and uploaded files take time to search and can exceed the LLM’s maximum token or word limit in a single call, so Data 360 breaks those sources into smaller parts called chunks.

More details and nuances

For Latin-based languages Data 360 approximates tokens as words, so a 512-word chunk typically fits the 512-token default. For non-Latin languages such as Japanese it counts punctuation marks instead, and 512 marks can exceed 512 tokens so leftover text never enters the embedding. Lower the max token limit for that content.