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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration
Prompt Engineering	15%	- Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks - Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting
Generative AI Models	20%	- Context and retrieval systems 1. Context windows and token limits 2. RAG, embeddings, vector databases - Model hosting and architecture 1. Cloud vs local deployment trade-offs 2. Latency, cost, privacy considerations - Model fundamentals and use cases 1. Use cases: summarization, code generation, content creation 2. LLMs and diffusion models overview
AI for Data Research and Analysis		- Data exploration and transformation 1. AI-assisted data cleaning 2. Insight generation and validation
AI for Code and Workflow Optimization		- AI-assisted software development 1. Code generation and debugging 2. SDLC integration - Automation workflows 1. Workflow orchestration concepts 2. API integration basics
Ethics and Security	15%	- AI security threats 1. Misinformation and hallucinations 2. AI-specific attack surfaces - Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - Data privacy and governance 1. Secure AI data handling

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Cisco AI Technical Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

A. implementation
B. testing
C. requirements
D. maintenance

Answer: C

Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.

NEW QUESTION # 21
What is the role of embeddings in AI systems?

A. Compressing logs
B. Assigning IP addresses
C. Encrypting traffic
D. Representing data numerically in vector space

Answer: D

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.

NEW QUESTION # 22
A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

A. few-shot prompting
B. zero-shot prompting
C. metaprompting
D. chain-of-thought

Answer: A

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.

NEW QUESTION # 23
What is fine-tuning in generative AI?

A. Formatting storage drives
B. Replacing CPUs
C. Training a pre-trained model on specialized data
D. Encrypting prompts

Answer: C

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 24
Why are embeddings essential to the retrieval phase of a RAG system?

A. They provide a keyword index.
B. They compress the prompt length.
C. They reduce model hallucination.
D. They represent semantic meaning.

Answer: D

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.

NEW QUESTION # 25
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