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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
What does NLP stand for in AI?

A. Natural Language Processing
B. Neural Link Protocol
C. Network Layer Processing
D. Native Logic Programming

Answer: A

Explanation:
NLP enables computers to understand and generate human language.

NEW QUESTION # 25
In an AI-powered automation workflow, what is the function of an approval gate?

A. to ensure the internal logic of AI remains closed for modification
B. to trigger automated completion notifications to project stakeholders
C. to automatically block deployments if system health alerts are active
D. to apply human judgement before high-impact actions are executed

Answer: D

Explanation:
An approval gate adds human review before an automated workflow performs a high-impact action, helping ensure that AI-driven decisions are checked for correctness, risk, and business impact before execution.

NEW QUESTION # 26
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 reduce the token count of the agent's system prompt
D. to map relationships between multiple tools in an agent workflow

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 # 27
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

A. Reduce GPU usage only
B. Encrypt AI datasets
C. Replace neural networks
D. Improve model responses with external knowledge

Answer: D

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.

NEW QUESTION # 28
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

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

Answer: C

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 # 29
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