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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q56-Q61):

NEW QUESTION # 56
What is the role of the client in the Model Context Protocol?

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

Answer: A

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.

NEW QUESTION # 57
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

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

Answer: A

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 # 58
Why are prompts important in generative AI systems?

A. They eliminate hallucinations completely
B. They replace training datasets
C. They encrypt AI hardware
D. They determine model behavior and output quality

Answer: D

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.

NEW QUESTION # 59
What is a use case for generative AI in the testing phase of the software development lifecycle?

A. generating synthetic test data
B. drafting user stories
C. generating automated documentation
D. performing static code analysis

Answer: A

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.

NEW QUESTION # 60
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

A. expanding the total context window
B. minimizing the input prompt size
C. increasing the model temperature
D. increasing the model sampling rate

Answer: B

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.

NEW QUESTION # 61
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