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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Ethics and Security	20%	- AI-specific threats, risks and misinformation - Responsible AI principles - Compliance and governance frameworks - Data privacy and security in AI systems
Topic 2: Data Research and Analysis	15%	- Data quality, bias and validation - Data collection, preparation and preprocessing - Data visualization and insight generation
Topic 3: AI for Code and Workflow Optimization	15%	- AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement
Topic 4: Prompt Engineering	15%	- Defensive prompting and error mitigation - Prompt design principles and best practices - Advanced prompting techniques
Topic 5: Generative AI Models	20%	- Context windows, token limits and response management - Model hosting options and trade-offs - Generative AI model families and use cases - Model selection and evaluation in AI hubs
Topic 6: 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

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Cisco AI Technical Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which concept helps reduce overfitting in machine learning?

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

Answer: D

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

NEW QUESTION # 66
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

A. AI-assisted debugging
B. continuous integration optimization
C. code scaffolding
D. automated unit testing

Answer: A

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.

NEW QUESTION # 67
In supervised machine learning, what is required?

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

Answer: D

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

NEW QUESTION # 68
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

A. to define the required parameters and data types the agent must return
B. to encrypt the payload before it is sent to the external service
C. to map relationships between multiple tools in an agent workflow
D. to reduce the token count of the agent's system prompt

Answer: A

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.

NEW QUESTION # 69
What is a difference between generative AI and agentic AI?

A. Generative AI operates autonomously and agentic AI requires a prompt for each action.
B. Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
C. Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.
D. Generative AI requires tool access and agentic AI relies on its training data.

Answer: C

Explanation:
Generative AI primarily creates content or responses from a prompt, while agentic AI can plan, reason, use tools, and execute multi-step workflows toward a goal.

NEW QUESTION # 70
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