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Cisco 810-110 Exam Syllabus Topics:

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

Cisco AI Technical Practitioner Sample Questions:

Question 1

What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to reduce the token count of the agent's system prompt
B. to define the required parameters and data types the agent must return
C. to encrypt the payload before it is sent to the external service
D. to map relationships between multiple tools in an agent workflow

Question 2

What does NLP stand for in AI?

A. Neural Link Protocol
B. Network Layer Processing
C. Natural Language Processing
D. Native Logic Programming

Question 3

What is aggregation bias in the context of AI-assisted data analysis?

A. when a summary masks subpatterns
B. when AI uses summary statistics instead of raw data
C. when AI fails to combine conflicting patterns
D. when data is omitted due to incompatible formats

Question 4

What is the main advantage of cloud-based AI services?

A. No need for networking
B. Unlimited free hardware
C. Scalability and flexible compute resources
D. Elimination of cybersecurity risks

Question 5

In an AI-powered automation workflow, what is deterministic branching?

A. A decision point where the next step is dictated by specific, pre-programmed rules.
B. A process where the AI model autonomously selects the next step based on its internal confidence score.
C. A workflow state that pauses execution until a human manually determines the next step.
D. A dynamic system where branching criteria evolve over time using optimization algorithms.

Solutions:

Question 1
Answer: B

Question 2
Answer: C

Question 3
Answer: A

Question 4
Answer: C

Question 5
Answer: A