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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

A. multimodal model
B. diffusion model
C. embedding model
D. generative adversarial network

Answer: A

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.

NEW QUESTION # 23
Which action demonstrates the principle of fairness in a generative AI deployment?

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

Answer: D

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 # 24
How does AI support exploratory data analysis when working with large, unstructured datasets?

A. by compressing the datasets
B. by identifying patterns and groupings
C. by performing bit-by-bit reconstruction
D. by tuning inference parameters

Answer: B

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.

NEW QUESTION # 25
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

A. removing system prompts
B. using smaller contexts
C. using known patterns
D. implementing constraints

Answer: D

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.

NEW QUESTION # 26
What is the purpose of tokenization in Large Language Models (LLMs)?

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

Answer: B

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

NEW QUESTION # 27
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