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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q32-Q37):

NEW QUESTION # 32
In supervised machine learning, what is required?

A. No data
B. Labeled datasets
C. Only encrypted traffic
D. Random hardware failures

Answer: B

Explanation:
Supervised learning trains models using labeled input-output pairs.

NEW QUESTION # 33
Why is defining a persona considered a best practice for technical prompt engineering?

A. It guides the tone and vocabulary.
B. It reduces the total token usage.
C. It provides a pattern to emulate.
D. It provides instructions for desired action.

Answer: A

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 34
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

A. minimizing the input prompt size
B. expanding the total context window
C. increasing the model temperature
D. increasing the model sampling rate

Answer: A

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.

NEW QUESTION # 35
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

A. maintenance
B. implementation
C. testing
D. requirements

Answer: D

Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.

NEW QUESTION # 36
Which action demonstrates the principle of fairness in a generative AI deployment?

A. requiring users to sign Acceptable Use Policies
B. hosting the model in a carbon-neutral data center
C. auditing model outputs for bias
D. redacting PII from the training dataset

Answer: C

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.

NEW QUESTION # 37
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