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810-110 Exam Topics and Domains
810-110 is organized into 6 weighted domains. Expect to work with AI coding assistants (e.g., Copilot-style tools), Anthropic Claude, Cloud model APIs, DALL-E, and more.
Generative AI Models
Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)
Describe major generative AI model families and common use cases
Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability)
Compare cloud-hosted vs locally hosted models and their trade-offs
Explain role of context windows, token limits and response management
Explain the 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)
Understand model selection in AI model hubs and repositories for appropriate use-cases
Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases
Describe RAG and the role of embeddings and vector databases
Prompt Engineering
Understand prompt engineering principles and patterns (roles, instructions, constraints)
Understand prompt engineering principles and patterns
Explain prompting techniques (iterative/sequential, chained, few-shot) and structures for text, image and audio generation
Explain prompting techniques and structures for text, image and audio generation
Describe prompt injection attack types
Describe prompt injection attack types
Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations)
Explain defensive prompting and mitigation strategies for AI-generated errors
Ethics and Security
Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety)
Explain responsible AI principles
Describe approaches to protect corporate data privacy and security in AI systems
Describe approaches to protect corporate data privacy and security in AI systems
Explain AI-specific security threats and risks, including misinformation
Explain AI-specific security threats and risks, including misinformation
Explain AI governance considerations (policy, risk management, compliance)
Explain AI governance considerations
Data Research and Analysis
Explain AI's role in exploratory data analysis (EDA)
Explain AI's role in exploratory data analysis (EDA)
Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning)
Describe automated data preparation tasks
Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure
Explain 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
Describe techniques for AI-assisted research, ideation, and content drafting
Development and Workflow Automation
Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment)
Describe AI's role across the software development lifecycle
Describe the AI capabilities for code generation and rapid prototyping
Describe the AI capabilities for code generation and rapid prototyping
Explain AI workflow design and monitoring principles
Explain AI workflow design and monitoring principles
Describe how token usage and context-window management affect prototyping cost, latency, and output quality
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)
Explain how AI improves code quality
Agentic AI
Differentiate Agentic AI from Generative AI use cases
Differentiate Agentic AI from Generative AI use cases
Explain AI agent design principles, autonomous capabilities, and orchestration
Explain AI agent design principles, autonomous capabilities, and orchestration
Describe Model Context Protocol (MCP) framework primitives in context of agentic AI
Describe Model Context Protocol (MCP) framework primitives in context of agentic AI
Explain human-in-the-loop (HITL) strategies
Explain human-in-the-loop (HITL) strategies
Describe data transformation and mapping within AI Agents
Describe data transformation and mapping within AI Agents
How do I earn this certification?
Passing 810-110 earns the Cisco Certified AI Technical Practitioner certification. It sits in the Artificial Intelligence track.
- 200-301 - CCNACommon associate-level foundation for broadening into Cisco's core technical tracks
- 350-401 - ENCORProfessional-level progression for candidates pursuing Cisco enterprise networking after building AI skills
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What's changed on this exam?
- ACTIVE
- Last content update: 2025 (v1.0 exam topics document)
Who should take this exam?
- Basic understanding of AI concepts
- Familiarity with technical workflows, APIs, and software development concepts
- Basic data handling and cloud services knowledge
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