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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
AI Ethics and Security	20%	- Responsible AI principles - Compliance and governance frameworks - Data privacy and security in AI systems - AI-specific threats, risks and misinformation
Prompt Engineering	15%	- Prompt design principles and best practices - Advanced prompting techniques - Defensive prompting and error mitigation
Data Research and Analysis	15%	- Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation
Generative AI Models	20%	- Context windows, token limits and response management - Generative AI model families and use cases - Model selection and evaluation in AI hubs - Model hosting options and trade-offs
AI for Code and Workflow Optimization	15%	- AI-assisted code generation and improvement - Automation and workflow enhancement - Integration of AI tools in development processes
Agentic AI	15%	- Agent design principles and capabilities - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework - Human-in-the-loop strategies

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Cisco AI Technical Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
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. metaprompting
C. zero-shot prompting
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 # 38
Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

A. executing legacy code refactoring
B. performing code scaffolding
C. providing inline co-pilot assistance
D. implementing predictive function logic

Answer: B

Explanation:
Code scaffolding uses AI to generate the initial structure and boilerplate for a new component, such as a complete Python class for an API client based on a high-level description.

NEW QUESTION # 39
Why are embeddings essential to the retrieval phase of a RAG system?

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

Answer: B

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 # 40
How does a coordinator agent function in a multi-agent AI framework?

A. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
B. It fine-tunes each agent's model weights to optimize performance on assigned tasks.
C. It serves as the external data source that each agent queries for grounding information.
D. It orchestrates task delegation across specialized agents and aggregates the outputs.

Answer: D

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.

NEW QUESTION # 41
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. automated unit testing
B. AI-assisted debugging
C. code scaffolding
D. continuous integration optimization

Answer: B

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 # 42
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