Cisco 810-110 AITECH Exam Overview

The 810-110 Cisco AI Technical Practitioner (AITECH) exam validates foundational technical knowledge and practical judgment in using generative AI, AI-assisted development tools, and agentic systems. It is designed for IT professionals, developers, data analysts, solutions architects, and technical practitioners who want to apply AI technologies to real-world business and engineering workflows.

Candidates should understand generative AI models, prompt engineering, responsible AI, data research and analysis, AI-assisted software development, workflow automation, and Agentic AI. The exam emphasizes selecting appropriate models, designing effective prompts, protecting sensitive data, evaluating AI-generated outputs, optimizing development workflows, and understanding how AI agents use tools and coordinate tasks under human oversight.

Passcert 810-110 AITECH Practice Tests help candidates review key AI concepts, identify knowledge gaps, and reinforce preparation alongside official Cisco learning resources, documentation, and practical AI exercises.

Cisco 810-110 Exam Information

810-110 Exam Domains and Skills Measured

Generative AI Models — 20%

This domain covers major generative AI model families, including large language models (LLMs) and diffusion models, and their common use cases. Candidates should understand cloud-hosted versus locally hosted models, context windows, token limits, model selection, Retrieval-Augmented Generation (RAG), embeddings, and vector databases. The focus is on choosing suitable models and architectures based on cost, latency, privacy, scalability, and task requirements.

Prompt Engineering — 15%

This domain focuses on designing effective prompts for text, image, and audio generation. Candidates should understand roles, instructions, constraints, iterative prompting, chained prompting, and few-shot techniques. They should also recognize prompt injection attacks and understand defensive prompting strategies for reducing hallucinations and improving response reliability.

Ethics and Security — 15%

This domain covers responsible AI principles, including fairness, transparency, accountability, bias mitigation, and safety. Candidates should understand corporate data privacy, AI-specific security threats, misinformation, governance, risk management, and compliance. Preparation should emphasize protecting sensitive information and validating AI outputs before using them in business or technical decisions.

Data Research and Analysis — 10%

This domain focuses on AI-assisted research, exploratory data analysis (EDA), data preparation, and content drafting. Candidates should understand how AI can help clean, format, transform, and analyze data, while recognizing privacy and ethical considerations. They should also know how to verify research findings and prevent sensitive information from being exposed.

Development and Workflow Automation — 20%

This domain covers the use of AI throughout the software development lifecycle, from requirements and prototyping to implementation, testing, and deployment. Candidates should understand AI-assisted code generation, rapid prototyping, debugging, error handling, documentation, workflow design, and monitoring. They should also understand how token usage and context-window management affect cost, latency, and output quality.

Agentic AI — 20%

This domain focuses on distinguishing Agentic AI from conventional generative AI use cases. Candidates should understand agent design, autonomous capabilities, orchestration, Model Context Protocol (MCP) primitives, human-in-the-loop (HITL) strategies, and data transformation within agent workflows. The emphasis is on understanding how agents use tools and execute tasks while maintaining appropriate oversight and control.

What Should You Study for the Cisco 810-110 Exam?

Preparation should cover six connected areas: model selection, prompt design, responsible AI, data analysis, AI-assisted development, and agentic workflows. Key study topics include LLMs, diffusion models, RAG, embeddings, vector databases, few-shot prompting, prompt injection, data privacy, AI-assisted data cleaning, code generation, workflow monitoring, MCP, and human oversight.

Candidates should focus on applying concepts to practical scenarios rather than memorizing definitions. For example, understand when RAG is appropriate, how to choose between cloud-hosted and local models, how to reduce prompt injection risks, and when an AI agent requires human approval before taking action.

How to Prepare for the Cisco 810-110 Exam

Understand Generative AI Models and RAG

Review model families, hosting options, context windows, token limits, embeddings, and vector databases. Practice identifying the most suitable model or architecture for different requirements involving cost, privacy, performance, and scalability.

Improve Prompt Engineering Skills

Practice writing prompts with clear roles, instructions, constraints, and examples. Learn how iterative and chained prompting can improve results, and understand how prompt injection and hallucinations affect the reliability of AI applications.

Study AI Ethics, Security, and Privacy

Review responsible AI principles, sensitive data handling, bias, misinformation, and governance. Understand why AI-generated responses require validation and how organizations can apply security and compliance controls.

Practice AI-Assisted Data Analysis and Development

Explore how AI supports data cleaning, research, content drafting, code generation, debugging, testing, and documentation. Learn to validate generated outputs and understand the trade-offs between token usage, response quality, latency, and cost.

Understand Agentic AI and MCP

Study the differences between generative AI and agentic systems, agent orchestration, tool use, data transformation, and human-in-the-loop controls. Focus on when autonomous execution is appropriate and when approval or supervision is necessary.

Use 810-110 Practice Tests to Measure Progress

Use practice questions to evaluate your understanding, review incorrect answers, and identify weaker domains. Combine practice tests with Cisco's official exam topics, Cisco U. learning resources, AI documentation, and hands-on exercises.

Who Should Take the Cisco 810-110 AITECH Exam?

The 810-110 exam is suitable for IT professionals, software developers, data analysts, network engineers, solutions architects, automation specialists, and technical team leaders who want to apply AI to their work.

Candidates do not need to be machine learning researchers or advanced AI developers. However, familiarity with technical workflows, data handling, software development concepts, APIs, and cloud services can help them understand the exam scenarios and apply AI concepts more effectively.

How Passcert Supports Your 810-110 Exam Preparation

Passcert 810-110 AITECH Practice Tests help candidates review generative AI models, prompt engineering, AI ethics and security, data research, development automation, and Agentic AI. The materials provide an additional way to assess understanding, identify knowledge gaps, and organize revision around the official exam objectives.

Combined with official Cisco learning resources, documentation, and practical AI exercises, Passcert practice materials provide additional support throughout your Cisco AI Technical Practitioner exam preparation journey.

Frequently Asked Questions About Cisco 810-110

What is the Cisco 810-110 AITECH exam?

The 810-110 AITECH exam is the assessment for the Cisco AI Technical Practitioner certification. It evaluates foundational knowledge of generative AI, prompt engineering, responsible AI, data analysis, AI-assisted development, and Agentic AI.

What topics are covered in the 810-110 exam?

The exam covers six domains: Generative AI Models, Prompt Engineering, Ethics and Security, Data Research and Analysis, Development and Workflow Automation, and Agentic AI.

What is the difference between generative AI and Agentic AI?

Generative AI produces outputs such as text, images, summaries, or code in response to instructions. Agentic AI can plan and execute multi-step tasks, use tools, and coordinate actions toward a defined objective. Candidates should understand both the differences and the situations in which each approach is appropriate.

Does the Cisco 810-110 exam require previous AI experience?

Cisco does not list a prerequisite certification on the official exam page. The certification is intended for technical practitioners who want to apply AI concepts in real-world workflows, although familiarity with software development, APIs, data, and cloud technologies can be beneficial.

How long is the Cisco 810-110 exam?

The exam lasts 60 minutes and is available in English. Cisco lists the exam fee as US$150 and uses pass/fail scoring.

Are 810-110 Practice Tests enough for exam preparation?

Practice tests are most effective when combined with the official Cisco exam topics, Cisco U. learning resources, documentation, and practical exercises. They can help identify knowledge gaps but should not replace understanding the underlying AI concepts and their real-world applications.