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Salesforce AI-201 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding - Agentforce Data Library types - Unstructured data processing: chunking, indexing |
| Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles |
| AI Agents | 35% | - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Topics and actions configuration - Agent User security and permissions - Agent types: Service, Sales, Employee |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Development Lifecycle | 15% | - Testing agents in Testing Center - Monitoring, adoption and optimization - Deployment: sandbox to production - Version control and maintenance |
Salesforce Certified Agentforce Specialist Sample Questions:
1. An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client. How should the Agentforce Specialist integrate the custom LLM into Salesforce?
A) Add the fine-tuned LLM in Einstein Studio Model Builder.
B) Enable model endpoint on OpenAl and make callouts to the model to generate emails.
C) Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
2. Universal Containers has created an Employee Agent. Which step should an Agentforce Specialist take to connect the agent with a Slack channel?
A) Create an Omni-Channel flow and connection between Salesforce and the Slack workspace.
B) Create a connection between Salesforce and the Slack workspace.
C) Create an embedded service deployment and connection between Salesforce and the Slack workspace.
3. What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?
A) Identify the best matching topic and actions and correct order of execution.
B) Determine a user's topic access and sort actions by priority to be executed.
C) Find similar requested topics and provide the actions that need to be executed.
4. Why is starting with a narrowly scoped Agentforce use case considered a best practice?
A) It reduces Salesforce license costs
B) It avoids the need for prompts
C) It limits risk and simplifies monitoring
D) It eliminates user training
5. Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
A) Case Escalation
B) Service Replies
C) Case Summaries
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |








