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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Generative AI Models	20%	- Common use cases 1. Text summarization 2. Content creation 3. Code generation - Major generative model families 1. Diffusion models 2. Large language models (LLMs) - 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 - Context windows and token management 1. Token limits 2. Response management - Retrieval Augmented Generation (RAG) 1. Vector databases 2. Embeddings 3. Grounding AI outputs in external knowledge
Topic 2: AI Ethics and Security		- Ethical considerations in AI 1. Transparency and explainability 2. Bias and fairness - Security threats and mitigations 1. Model security 2. Data privacy
Topic 3: AI for Code and Workflow Optimization		- Workflow automation 1. Process automation 2. Task optimization - AI-assisted code development 1. Code review and debugging 2. Code generation
Topic 4: Agentic AI	20%	- Differentiation from Generative AI 1. Autonomous capabilities 2. Use cases comparison - AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration - Data transformation and mapping 1. Within AI Agents - Model Context Protocol (MCP) 1. MCP framework primitives 2. Context in agentic AI
Topic 5: Prompt Engineering	15%	- Prompt structures 1. Image generation 2. Audio generation 3. Text generation - Principles and patterns 1. Constraints 2. Roles 3. Instructions - Security and safety 1. Prompt injection attacks 2. Defensive prompting methods 3. Reducing hallucinations 4. Improving response accuracy - Prompting techniques 1. Chained prompting 2. Few-shot prompting 3. Iterative prompting
Topic 6: 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

Cisco AI Technical Practitioner Sample Questions:

Question 1

Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

A. removing negative constraints
B. requesting citations for the summary
C. using a short system prompt
D. increasing the model creativity

Question 2

How does AI support exploratory data analysis when working with large, unstructured datasets?

A. by performing bit-by-bit reconstruction
B. by compressing the datasets
C. by tuning inference parameters
D. by identifying patterns and groupings

Question 3

Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

A. large language models
B. diffusion models
C. generative adversarial networks
D. variational autoencoders

Question 4

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

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

Question 5

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.

Solutions:

Question 1
Answer: B

Question 2
Answer: D

Question 3
Answer: A

Question 4
Answer: B

Question 5
Answer: A