A great way to start the Cisco AI Technical Practitioner (AITECH) preparation is to begin by properly appreciating the role that syllabus and study guide play in the Cisco 810-110 certification exam. This study guide is an instrument to get you on the same page with Cisco and understand the nature of the Cisco AI Technical Practitioner exam.
Our team of experts has composed this Cisco 810-110 exam preparation guide to provide the overview about Cisco AI Technical Practitioner exam, study material, sample questions, practice exam and ways to interpret the exam objectives to help you assess your readiness for the Cisco AITECH exam by identifying prerequisite areas of knowledge. We recommend you to refer the simulation questions and practice test listed in this guide to determine what type of questions will be asked and the level of difficulty that could be tested in the Cisco AI Technical Practitioner certification exam.
Cisco 810-110 Exam Overview:
| Exam Name | Cisco AI Technical Practitioner |
| Exam Number | 810-110 AITECH |
| Exam Price | $150 USD |
| Duration | 60 minutes |
| Number of Questions | 40-60 |
| Passing Score | Variable (750-850 / 1000 Approx.) |
| Recommended Training | Cisco AI Technical Practitioner | AITECH |
| Exam Registration | PEARSON VUE |
| Sample Questions | Cisco 810-110 Sample Questions |
| Practice Exam | Cisco AI Technical Practitioner Practice Test |
Cisco 810-110 Exam Topics:
| Section | Weight | Objectives |
|---|---|---|
| Generative AI Models | 20% |
- Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation) - Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability) - Explain role of context windows, token limits and response management - Understand model selection in AI model hubs and repositories for appropriate use‑cases (e.g., reasoning, multimodality) - Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases |
| Prompt Engineering | 15% |
- Understand prompt engineering principles and patterns (roles, instructions, constraints) - Explain prompting techniques (iterative/sequential, chained, few‑shot) and structures for text, image and audio generation - Describe prompt injection attack types - Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations) |
| Ethics and Security | 15% |
- Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety) - Describe approaches to protect corporate data privacy and security in AI systems - Explain AI-specific security threats and risks, including misinformation - Explain AI governance considerations (policy, risk management, compliance) |
| Data Research and Analysis | 10% |
- Explain AI’s role in exploratory data analysis (EDA) - Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning) - Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure - Describe techniques for AI-assisted research, ideation, and content drafting |
| Development and Workflow Automation | 20% |
- Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment) - Describe the AI capabilities for code generation and rapid prototyping - Explain AI workflow design and monitoring principles - Describe how token usage and context‑window management affect prototyping cost, latency, and output quality - Explain how AI improves code quality (debugging assistance, error handling, documentation) |
| Agentic AI | 20% |
- Differentiate Agentic AI from Generative AI use cases - Explain AI agent design principles, autonomous capabilities, and orchestration - Describe Model Context Protocol (MCP) framework primitives in context of agentic AI - Explain human-in-the-loop (HITL) strategies - Describe data transformation and mapping within AI Agents |
Cisco AITECH Exam Description:
Cisco AI Technical Practitioner v1.0 (AITECH 810-110) is a 60-minute exam associated with the AI Technical Practitioner certification. This exam tests a candidate's knowledge and skills related to generative AI models, prompt engineering, AI ethics and security, data research and analysis, AI for Code and Workflow Optimization, and Agentic AI. The course, Cisco AI Technical Practitioner (AITECH), helps candidates prepare for this exam.
