Cisco AI Technical Practitioner Study Hub
Exam Code
810-110
Blueprint Items
27
Articles Live
72
Coverage
78% of blueprint covered
Generative AI Models
1.1
Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation) — LLMs and their use cases (text summarization, content creation, code generation); diffusion models and image generation use cases
⌛ Common Use Cases for Generative AI Models
· Coming 2 August 2026
1.2
Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability) — Cloud-hosted vs locally hosted trade-offs: cost, latency, privacy, scalability
⌛ Privacy and Security in AI Model Hosting
· Coming 4 August 2026
1.3
Explain role of context windows, token limits and response management — Role of context windows; token limits and their impact; response management strategies
1.4
Understand model selection in AI model hubs and repositories for appropriate use-cases (e.g., reasoning, multimodality) — Model selection criteria; AI model hubs and repositories; use-cases including reasoning and multimodality
1.5
Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases — RAG architecture and how it works; role of embeddings; vector databases and their function
Prompt Engineering
2.1
Understand prompt engineering principles and patterns (roles, instructions, constraints) — Prompt engineering principles; patterns including roles, instructions, and constraints
⌛ Understanding Constraints in Prompt Engineering
· Coming 23 July 2026
2.2
Explain prompting techniques (iterative/sequential, chained, few-shot) and structures for text, image and audio generation — Iterative and sequential prompting; chained prompting; few-shot techniques; structures for text, image and audio generation
2.3
Describe prompt injection attack types — Direct prompt injection; indirect prompt injection; jailbreaking techniques and risks
2.4
Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations) — Defensive prompting strategies; hallucination causes and mitigation; output validation approaches
Ethics and Security
3.1
Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety) — Fairness and bias mitigation; transparency and explainability; accountability frameworks; safety considerations
3.2
Describe approaches to protect corporate data privacy and security in AI systems — Data privacy controls in AI systems; preventing data leakage; corporate data protection approaches
⌛ Comparing Data Privacy Tools for AI Systems
· Coming 9 August 2026
3.3
Explain AI-specific security threats and risks, including misinformation — AI-specific threats including model poisoning and adversarial attacks; misinformation and deepfake risks
3.4
Explain AI governance considerations (policy, risk management, compliance) — AI governance policy frameworks; risk management for AI systems; regulatory compliance considerations
⌛ AI Governance vs. Risk Management: Key Differences
· Coming 21 July 2026
Data Research and Analysis
4.1
Explain AI's role in exploratory data analysis (EDA) — AI tools for exploratory data analysis; pattern recognition; anomaly detection; data summarisation
4.2
Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning) — Automated quality checks; data formatting and transformation; data cleaning techniques
⌛ Comparing AI Tools for Data Preparation: Which One Suits Your Needs?
· Coming 6 August 2026
4.3
Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure — Ethical considerations in AI data analysis; privacy controls; preventing data exposure in AI workflows
4.4
Describe techniques for AI-assisted research, ideation, and content drafting — AI-assisted research techniques; ideation with AI tools; content drafting and summarisation workflows
Development and Workflow Automation
5.1
Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment) — AI across SDLC stages: requirements, prototyping, implementation, testing, deployment
5.2
Describe the AI capabilities for code generation and rapid prototyping — AI code generation tools and capabilities; rapid prototyping with AI assistance; code completion and generation
5.3
Explain AI workflow design and monitoring principles — AI workflow design principles; monitoring AI workflows; performance and reliability considerations
5.4
Describe how token usage and context-window management affect prototyping cost, latency, and output quality — Token usage optimisation; context-window management strategies; impact on cost, latency, and output quality
⌛ Token Usage vs Context-Window: Which Matters More?
· Coming 19 July 2026
5.5
Explain how AI improves code quality (debugging assistance, error handling, documentation) — AI-assisted debugging; error handling improvements; automated documentation generation
⌛ How AI Enhances Debugging for IT Professionals
· Coming 26 July 2026
⌛ Improving Error Handling with AI: A Practical Guide
· Coming 28 July 2026
⌛ Automating Documentation with AI: Benefits and Tools
· Coming 30 July 2026
Browse All Cisco AI Technical Practitioner Articles
Last updated 19 July 2026