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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering	15%	- Prompting techniques 1. Chained prompting 2. Few-shot prompting 3. Iterative prompting - Prompt structures 1. Text generation 2. Audio generation 3. Image generation - Principles and patterns 1. Constraints 2. Instructions 3. Roles - Security and safety 1. Prompt injection attacks 2. Improving response accuracy 3. Defensive prompting methods 4. Reducing hallucinations
Topic 2: AI for Code and Workflow Optimization		- AI-assisted code development 1. Code review and debugging 2. Code generation - Workflow automation 1. Process automation 2. Task optimization
Topic 3: AI Ethics and Security		- Security threats and mitigations 1. Model security 2. Data privacy - Ethical considerations in AI 1. Transparency and explainability 2. Bias and fairness
Topic 4: Generative AI Models	20%	- Common use cases 1. Content creation 2. Code generation 3. Text summarization - Context windows and token management 1. Token limits 2. Response management - Major generative model families 1. Large language models (LLMs) 2. Diffusion models - Model selection 1. Reasoning and multimodal tasks 2. Selection from AI hubs - Retrieval Augmented Generation (RAG) 1. Embeddings 2. Vector databases 3. Grounding AI outputs in external knowledge - Model hosting strategies 1. Cloud-based vs. local deployments 2. Cost, latency, privacy, and scalability considerations
Topic 5: Agentic AI	20%	- AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration - 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
Topic 6: Data Research and Analysis		- Data analysis techniques 1. Statistical analysis 2. Data visualization - Data collection and preparation 1. Data gathering methodologies 2. Data cleaning and preprocessing

Cisco AI Technical Practitioner Sample Questions:

Question 1

Which approach protects PII when utilizing a public AI service for data analysis?

A. encrypting the data payload during transit to the AI service provider
B. ensuring the AI provider maintains industry-standard SOC 2 compliance
C. utilizing system prompts to instruct the AI to ignore sensitive data fields
D. anonymizing or redacting PH from the dataset before transmitting the request

Question 2

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

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

Question 3

Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

A. performing code scaffolding
B. implementing predictive function logic
C. executing legacy code refactoring
D. providing inline co-pilot assistance

Question 4

What is a difference between generative AI and agentic AI?

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

Question 5

Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. to allow the AI agent to bypass the integrity constraints of the database
B. to provide administrative credentials required to bypass MFA requirements
C. to allow the AI agent to automatically resolve transaction deadlocks
D. to serve as a safety gate against unintended data modifications

Solutions:

Question 1
Answer: D

Question 2
Answer: B

Question 3
Answer: A

Question 4
Answer: C

Question 5
Answer: D