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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering	15%	- Defensive prompting and error mitigation - Prompt design principles and best practices - Advanced prompting techniques
Topic 2: Agentic AI	15%	- Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases - Agent design principles and capabilities
Topic 3: AI for Code and Workflow Optimization	15%	- Automation and workflow enhancement - Integration of AI tools in development processes - AI-assisted code generation and improvement
Topic 4: Data Research and Analysis	15%	- Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing
Topic 5: Generative AI Models	20%	- Generative AI model families and use cases - Context windows, token limits and response management - Model hosting options and trade-offs - Model selection and evaluation in AI hubs
Topic 6: AI Ethics and Security	20%	- Responsible AI principles - AI-specific threats, risks and misinformation - Compliance and governance frameworks - Data privacy and security in AI systems

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

NEW QUESTION # 56
A team uses AI to scaffold a new microservice. What does this process involve?

A. removing redundant logic
B. generating the initial structure
C. creating automated deployment scripts
D. testing the final production code

Answer: B

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.

NEW QUESTION # 57
What is a use case for generative AI in the testing phase of the software development lifecycle?

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

Answer: D

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 # 58
What is the purpose of tokenization in Large Language Models (LLMs)?

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

Answer: C

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

NEW QUESTION # 59
How does context window management help control the cost of a long-running AI conversation?

A. by capping the cumulative number of input tokens sent with each new prompt
B. by switching to an algorithm that reduces the character-to-token ratio
C. by automatically switching to a lower-cost model as the conversation history grows
D. by increasing the context window size to reduce the total number of API calls

Answer: A

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 60
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

A. It ensures the most creative text.
B. It allows for easy repurposing.
C. It maintains contextual alignment.
D. It minimizes token consumption.

Answer: B

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.

NEW QUESTION # 61
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