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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Data Research and Analysis		- Data collection and preparation 1. Data gathering methodologies 2. Data cleaning and preprocessing - Data analysis techniques 1. Data visualization 2. Statistical analysis
Generative AI Models	20%	- Model selection 1. Selection from AI hubs 2. Reasoning and multimodal tasks - Model hosting strategies 1. Cost, latency, privacy, and scalability considerations 2. Cloud-based vs. local deployments - Major generative model families 1. Diffusion models 2. Large language models (LLMs) - Context windows and token management 1. Response management 2. Token limits - Common use cases 1. Text summarization 2. Content creation 3. Code generation - Retrieval Augmented Generation (RAG) 1. Embeddings 2. Grounding AI outputs in external knowledge 3. Vector databases
AI Ethics and Security		- Ethical considerations in AI 1. Bias and fairness 2. Transparency and explainability - Security threats and mitigations 1. Data privacy 2. Model security
AI for Code and Workflow Optimization		- AI-assisted code development 1. Code review and debugging 2. Code generation - Workflow automation 1. Task optimization 2. Process automation
Agentic AI	20%	- Model Context Protocol (MCP) 1. MCP framework primitives 2. Context in agentic AI - Differentiation from Generative AI 1. Autonomous capabilities 2. Use cases comparison - Data transformation and mapping 1. Within AI Agents - AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration
Prompt Engineering	15%	- Principles and patterns 1. Constraints 2. Roles 3. Instructions - Prompting techniques 1. Few-shot prompting 2. Chained prompting 3. Iterative prompting - Security and safety 1. Improving response accuracy 2. Prompt injection attacks 3. Defensive prompting methods 4. Reducing hallucinations - Prompt structures 1. Audio generation 2. Text generation 3. Image generation

Cisco AI Technical Practitioner Sample Questions:

Question #1

What is the role of an MCP server in the MCP open standard?

A. to expose tools and resources to an AI model
B. to host the LLM and perform token inference
C. to provide the GUI for the end-user interaction
D. to route requests between different AI providers

Reveal Solution Discussion 0

Correct Answer: A 🗳️