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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q13-Q18):

NEW QUESTION # 13
What is the purpose of tokenization in Large Language Models (LLMs)?

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

Answer: A

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

NEW QUESTION # 14
A team uses AI to scaffold a new microservice. What does this process involve?

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

Answer: D

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 # 15
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

A. misinformation
B. data poisoning
C. disinformation
D. knowledge cut-off

Answer: D

Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.

NEW QUESTION # 16
Which concept helps reduce overfitting in machine learning?

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

Answer: A

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

NEW QUESTION # 17
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

A. managed SaaS AI platform with SOC 2 audit
B. hybrid deployment with cloud-based backup
C. local LLM deployment on internal hardware
D. cloud-based API with Zero Data Retention

Answer: C

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.

NEW QUESTION # 18
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