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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
What happens when the input provided to an LLM exceeds its context window limit?

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

Answer: D

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 # 64
What is the role of embeddings in AI systems?

A. Encrypting traffic
B. Representing data numerically in vector space
C. Compressing logs
D. Assigning IP addresses

Answer: B

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.

NEW QUESTION # 65
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

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

Answer: A

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 # 66
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 # 67
Which role does AI play in assisting with the refactoring of software code?

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

Answer: A

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 # 68
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