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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
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
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
AI Ethics and Security	20%	- Compliance and governance frameworks - AI-specific threats, risks and misinformation - Data privacy and security in AI systems - Responsible AI principles
Prompt Engineering	15%	- Defensive prompting and error mitigation - Prompt design principles and best practices - Advanced prompting techniques
Data Research and Analysis	15%	- Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing
AI for Code and Workflow Optimization	15%	- Integration of AI tools in development processes - AI-assisted code generation and improvement - Automation and workflow enhancement

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Cisco AI Technical Practitioner Sample Questions (Q48-Q53):

NEW QUESTION # 48
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

A. system safety
B. inclusiveness
C. bias mitigation
D. transparency

Answer: D

Explanation:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.

NEW QUESTION # 49
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to reduce the token count of the agent's system prompt
B. to encrypt the payload before it is sent to the external service
C. to map relationships between multiple tools in an agent workflow
D. to define the required parameters and data types the agent must return

Answer: D

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

NEW QUESTION # 50
Which concept helps reduce overfitting in machine learning?

A. Increasing random noise only
B. Regularization
C. Ignoring datasets
D. Disabling validation

Answer: B

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.

NEW QUESTION # 51
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

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

Answer: A

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 # 52
What is the role of an MCP server in the MCP open standard?

A. to provide the GUI for the end-user interaction
B. to expose tools and resources to an AI model
C. to host the LLM and perform token inference
D. to route requests between different AI providers

Answer: B

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.

NEW QUESTION # 53
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