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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ethics and Security	15%	- Responsible AI principles 1. Bias mitigation 2. Fairness, transparency, accountability - AI security threats 1. AI-specific attack surfaces 2. Misinformation and hallucinations - Data privacy and governance 1. Secure AI data handling
Topic 2: Generative AI Models	20%	- 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 - Context and retrieval systems 1. RAG, embeddings, vector databases 2. Context windows and token limits
Topic 3: AI for Code and Workflow Optimization		- Automation workflows 1. Workflow orchestration concepts 2. API integration basics - AI-assisted software development 1. Code generation and debugging 2. SDLC integration
Topic 4: Prompt Engineering	15%	- Prompt design principles 1. Roles, instructions, constraints - Prompting techniques 1. Chained and iterative prompting 2. Few-shot prompting - Security and robustness 1. Mitigation and defensive prompting 2. Prompt injection risks
Topic 5: AI for Data Research and Analysis		- Data exploration and transformation 1. Insight generation and validation 2. AI-assisted data cleaning
Topic 6: Agentic AI Systems		- Agent architecture 1. Multi-step reasoning workflows 2. Autonomous agents and orchestration

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Cisco AI Technical Practitioner Sample Questions (Q57-Q62):

NEW QUESTION # 57
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. chain-of-thought
B. zero-shot prompting
C. few-shot prompting
D. metaprompting

Answer: C

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 # 58
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

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

Answer: A

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 # 59
What is the purpose of tokenization in Large Language Models (LLMs)?

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

Answer: D

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

NEW QUESTION # 60
Why is defining a persona considered a best practice for technical prompt engineering?

A. It provides instructions for desired action.
B. It reduces the total token usage.
C. It guides the tone and vocabulary.
D. It provides a pattern to emulate.

Answer: C

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 61
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 serve as a safety gate against unintended data modifications
C. to allow the AI agent to automatically resolve transaction deadlocks
D. to provide administrative credentials required to bypass MFA requirements

Answer: B

Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.

NEW QUESTION # 62
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