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Cisco 810-110 Exam Syllabus Topics:

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

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810-110 Valid Study Materials - 810-110 Valid Exam Syllabus

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Cisco AI Technical Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
What is occurring when an agent converts a CSV list into a Markdown table for a final report?

A. data filtering
B. data classification
C. data quantization
D. data transformation

Answer: D

Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.

NEW QUESTION # 46
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. increasing the model creativity
C. removing negative constraints
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 # 47
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 ensures the most creative text.
D. It minimizes token consumption.

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 # 48
What is fine-tuning in generative AI?

A. Formatting storage drives
B. Training a pre-trained model on specialized data
C. Encrypting prompts
D. Replacing CPUs

Answer: B

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.

NEW QUESTION # 49
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. performing code scaffolding
B. executing legacy code refactoring
C. implementing predictive function logic
D. providing inline co-pilot assistance

Answer: A

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 # 50
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