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Cisco 810-110 Exam Syllabus Topics:

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

Cisco AI Technical Practitioner Sample Questions:

1. What is a difference between generative AI and agentic AI?

A) Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.
B) Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
C) Generative AI requires tool access and agentic AI relies on its training data.
D) Generative AI operates autonomously and agentic AI requires a prompt for each action.

2. In an AI-powered automation workflow, what is the function of an approval gate?

A) to ensure the internal logic of AI remains closed for modification
B) to apply human judgement before high-impact actions are executed
C) to trigger automated completion notifications to project stakeholders
D) to automatically block deployments if system health alerts are active

3. Which activity is an indirect prompt injection attack?

A) A prompt forces AI to ignore safety filters.
B) A user types a malicious command.
C) The model generates a false fact.
D) AI reads a poisoned data source.

4. Which AI concern focuses on protecting sensitive information?

A) Clock synchronization
B) VLAN tagging
C) Data privacy
D) Packet fragmentation

5. A team uses AI to scaffold a new microservice. What does this process involve?

A) generating the initial structure
B) removing redundant logic
C) testing the final production code
D) creating automated deployment scripts

Solutions:

Question # 1
Answer: A

Question # 2
Answer: B

Question # 3
Answer: D

Question # 4
Answer: C

Question # 5
Answer: A

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