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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which Cisco platform provides AI-powered security analytics?

A. Cisco SecureX
B. Cisco Packet Tracer
C. Cisco IOS ROMMON
D. Cisco UCS Manager only

Answer: A

Explanation:
Cisco SecureX integrates security visibility, analytics, and automation using AI-driven insights.

NEW QUESTION # 45
What is the role of the client in the Model Context Protocol?

A. to act as an LLM for inferencing
B. to act as a tool or service provider
C. to orchestrate interactions with servers
D. to provide resources for task execution

Answer: C

NEW QUESTION # 46
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

A. using few-shot
B. describing the situation
C. stating the purpose
D. defining a persona

Answer: B

Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.

NEW QUESTION # 47
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

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

Answer: C

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.

NEW QUESTION # 48
What happens when the input provided to an LLM exceeds its context window limit?

A. The model loses the earliest part of the text.
B. The model increases its context window size.
C. The model switches to a different algorithm.
D. The model caches the overflow data.

Answer: A

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 49
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