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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q59-Q64):

NEW QUESTION # 59
What is the purpose of tokenization in Large Language Models (LLMs)?

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

Answer: C

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

NEW QUESTION # 60
In an AI-powered automation workflow, what is deterministic branching?

A. A decision point where the next step is dictated by specific, pre-programmed rules.
B. A process where the AI model autonomously selects the next step based on its internal confidence score.
C. A workflow state that pauses execution until a human manually determines the next step.
D. A dynamic system where branching criteria evolve over time using optimization algorithms.

Answer: A

Explanation:
Deterministic branching uses predefined rules or conditions to decide the next workflow step, making the path predictable and repeatable rather than model-driven or manually selected.

NEW QUESTION # 61
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

A. implementation
B. testing
C. maintenance
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 # 62
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. diffusion model
B. generative adversarial network
C. multimodal model
D. embedding model

Answer: C

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 # 63
What happens when the input provided to an LLM exceeds its context window limit?

A. The model increases its context window size.
B. The model loses the earliest part of the text.
C. The model caches the overflow data.
D. The model switches to a different algorithm.

Answer: B

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.

NEW QUESTION # 64
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