Cisco 810-110 Certification Exam Syllabus
Preparing for the Cisco AI Technical Practitioner (AITECH) exam starts with knowing exactly what is assessed. This NWExam syllabus guide brings together the Cisco 810-110 exam details, syllabus topics, recommended training resources, and preparation references in one place, organized around the knowledge areas the certification tests, so you can plan your study with confidence.
A syllabus shows you what to study; effective preparation confirms you can understand and apply those topics when answering certification-style questions. Begin with the official Cisco objectives, documentation, and study materials, then use structured self-assessment and hands-on practice to identify knowledge gaps, reinforce weaker areas, and improve your readiness before attempting the Cisco 810-110 exam.
Cisco 810-110 Exam Summary and Key Details
| 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 810-110 Practice Exam |
Cisco 810-110 Exam Syllabus 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 |
Understanding the Cisco AITECH Certification Exam
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.
Use the syllabus objectives as the foundation of your study plan. Official Cisco training and documentation will help you build the required knowledge, while hands-on experience reinforces practical understanding. Once the core topics are covered, structured practice lets you evaluate your readiness, recognize areas that need further review, and become more comfortable working through exam-oriented questions in a timed environment. Practice exams should complement, not replace, official training, documentation, and hands-on learning.
For additional exam and preparation information, review the Cisco 810-110 certification FAQs. When you are ready to move from syllabus review to repeated timed practice, you can access the Premium Cisco 810-110 practice exam. For the latest certification requirements, policies, pricing, and vendor updates, always refer to the official Cisco 810-110 exam page.
- Cisco AI Technical Practitioner |
- AI Technical Practitioner Certification Syllabus |
- Cisco AI Technical Practitioner Study Guide |
- Cisco 810-110 Syllabus |
- 810-110 Exam Topics |
- Cisco 810-110 AITECH Exam Cost |
- Cisco 810-110 Passing Score |
- Cisco 810-110 Exam Duration |
- Cisco 810-110 Registration |
- Cisco 810-110 Sample Questions |
- 810-110 Exam Cost
