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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which role does AI play in assisting with the refactoring of software code?

A. reducing manual code documentation
B. identifying flaws and improving design
C. creating a new database schema
D. integrating a new API

Answer: B

Explanation:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.

NEW QUESTION # 47
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

A. continuous integration optimization
B. code scaffolding
C. automated unit testing
D. AI-assisted debugging

Answer: D

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.

NEW QUESTION # 48
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 produces single-turn responses and agentic AI executes multi-step reasoning loops.
C. Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
D. Generative AI requires tool access and agentic AI relies on its training data.

Answer: B

Explanation:
Generative AI primarily creates content or responses from a prompt, while agentic AI can plan, reason, use tools, and execute multi-step workflows toward a goal.

NEW QUESTION # 49
Which Cisco AI capability helps detect abnormal network behavior?

A. AI-driven anomaly detection
B. BIOS recovery mode
C. Static routing
D. Manual packet counting

Answer: A

Explanation:
AI systems can identify unusual patterns and potential threats automatically.

NEW QUESTION # 50
What is the purpose of tokenization in Large Language Models (LLMs)?

A. Splitting text into manageable units
B. Compressing images
C. Routing packets
D. Encrypting passwords

Answer: A

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.

NEW QUESTION # 51
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