Salesforce Certified AI Specialist Practice Test
Build your confidence for Salesforce Certified AI Specialist. Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
The Salesforce Certified AI Specialist Practice Test is designed for professionals seeking to validate their expertise in applying artificial intelligence within the Salesforce ecosystem. This exam covers five core domains: the Einstein Trust Layer, Generative AI in CRM Applications, Model Builder, Prompt Builder, and Salesforce AI Features. It tests a candidate's ability to configure, manage, and troubleshoot AI-driven tools that enhance customer relationship management. The ideal test-taker includes Salesforce administrators, developers, consultants, and architects who have hands-on experience with Salesforce AI products and are looking to demonstrate their proficiency in deploying responsible, scalable AI solutions. By completing this practice test, learners will gain a clear understanding of exam structure, question formats, and the depth of knowledge required to pass the official certification. They will also build confidence in applying AI concepts such as data masking, prompt engineering, model selection, and trust layer governance to real-world business scenarios. This practice test serves as both a diagnostic tool and a study accelerator, helping candidates identify knowledge gaps and focus their preparation efforts effectively.
Sample Questions
Choose an answer and explore the explanation to see how practice works.
A project lead asks how to handle creating and managing agents programmatically from Python. What is the most appropriate response?
A separate implementation team needs to handle supporting escalation from an Agentforce agent to a human service representative. Which answer is best?
During an implementation review, the requirement is integrating Agentforce into a mobile app. Which answer best fits?
During an implementation review, the requirement is programmatically creating, managing, and deploying AI agents in Salesforce. Which answer best fits?
A delivery team wants a source-grounded design for chatting with agents through REST. Which choice is correct?
Career Opportunities & Salary
Exam insights and study advice
Mastering the content in this practice test directly translates to the ability to implement AI solutions that are secure, ethical, and impactful in a production CRM environment. The Einstein Trust Layer ensures customer data privacy and compliance, which is non-negotiable for enterprises handling sensitive information. Generative AI in CRM enables personalized customer interactions at scale, while Model Builder and Prompt Builder allow for custom AI workflows without extensive coding. These skills are increasingly demanded by employers seeking to leverage AI for sales forecasting, service automation, and marketing personalization. Passing the actual certification signals to stakeholders that you can responsibly integrate AI into business processes, reducing risk and accelerating time-to-value.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.
01AI Agents
Topics
- Explain how an agent works and its basic building blocks of agent script.
- Explain the components and benefits of hybrid reasoning, including how Agent Script functions in Canvas and Script View.
- Given a use case, manage deterministic behavior for the agent using mechanisms like filters, variables, and template expressions.
- Given a scenario, select and configure standard topics, custom topics, standard Agent actions, and custom Agent actions.
- Explain the process for connecting agents to various channels such as digital experience, email, voice, and Slack.
- Explain the security context in which the agent is actually running, and how it impacts agent action execution.
- Given a scenario, identify when to use an Employee or Service agent.
- Given a scenario, identify when it's appropriate to use Agent API.
Learning objectives
- Explain how an agent works and its basic building blocks of agent script.
- Explain the components and benefits of hybrid reasoning, including how Agent Script functions in Canvas and Script View.
- Given a use case, manage deterministic behavior for the agent using mechanisms like filters, variables, and template expressions.
- Given a scenario, select and configure standard topics, custom topics, standard Agent actions, and custom Agent actions.
- Explain the process for connecting agents to various channels such as digital experience, email, voice, and Slack.
- Explain the security context in which the agent is actually running, and how it impacts agent action execution.
- Given a scenario, identify when to use an Employee or Service agent.
- Given a scenario, identify when it's appropriate to use Agent API.
02Data 360 Fundamentals
Topics
- Explain the considerations of Agentforce Data Library and its concepts.
- Explain foundational concepts of Data 360 such as chunking, indexing, and retrievers.
Learning objectives
- Explain the considerations of Agentforce Data Library and its concepts.
- Explain foundational concepts of Data 360 such as chunking, indexing, and retrievers.
03Prompt Engineering
Topics
- Given business requirements, identify when it's appropriate to use Prompt Builder.
- Identify access controls governing prompt templates.
- Identify the considerations for using a prompt template type such as field generation and flex types.
- Given a scenario, identify the appropriate grounding technique.
- Explain the process for creating, activating, and executing prompt templates.
- Explain how to implement best practices for writing effective prompts.
- Identify the security and privacy features of the Trust Layer.
- Explain how to manage and prevent specific models from being accessed.
Learning objectives
- Given business requirements, identify when it's appropriate to use Prompt Builder.
- Identify access controls governing prompt templates.
- Identify the considerations for using a prompt template type such as field generation and flex types.
- Given a scenario, identify the appropriate grounding technique.
- Explain the process for creating, activating, and executing prompt templates.
- Explain how to implement best practices for writing effective prompts.
- Identify the security and privacy features of the Trust Layer.
- Explain how to manage and prevent specific models from being accessed.
04Governance & Observability
Topics
- Explain the process for managing and monitoring agents.
- Explain agent analytics and agent optimization
Learning objectives
- Explain the process for managing and monitoring agents.
- Explain agent analytics and agent optimization
05Testing, Deployment, & Maintenance
Topics
- Given a scenario, test an agent using Testing Center.
- Explain how Testing Center evaluations work.
- Identify the considerations for deploying an agent from sandbox to production.
- Identify the considerations for deploying a template from sandbox to production.
Learning objectives
- Given a scenario, test an agent using Testing Center.
- Explain how Testing Center evaluations work.
- Identify the considerations for deploying an agent from sandbox to production.
- Identify the considerations for deploying a template from sandbox to production.
06Multi-Agent Orchestration
Topics
- Given a scenario, determine whether a Single-Agent (SOMA) architecture is appropriate for scalability and control.
- Explain the purpose of existing open standard multi-agent protocols such as MCP and A2A.
Learning objectives
- Given a scenario, determine whether a Single-Agent (SOMA) architecture is appropriate for scalability and control.
- Explain the purpose of existing open standard multi-agent protocols such as MCP and A2A.