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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q66-Q71):

NEW QUESTION # 66
What is the role of the client in the Model Context Protocol?

A. to act as a tool or service provider
B. to orchestrate interactions with servers
C. to act as an LLM for inferencing
D. to provide resources for task execution

Answer: B

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.

NEW QUESTION # 67
What is occurring when an agent converts a CSV list into a Markdown table for a final report?

A. data filtering
B. data transformation
C. data quantization
D. data classification

Answer: B

Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.

NEW QUESTION # 68
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

A. to serve as a safety gate against unintended data modifications
B. to allow the AI agent to automatically resolve transaction deadlocks
C. to provide administrative credentials required to bypass MFA requirements
D. to allow the AI agent to bypass the integrity constraints of the database

Answer: A

Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.

NEW QUESTION # 69
Which AI model type is best suited for image recognition tasks?

A. K-Means
B. Linear Regression
C. Convolutional Neural Network (CNN)
D. Decision Tree only

Answer: C

Explanation:
CNNs are optimized for image and visual pattern recognition.

NEW QUESTION # 70
Why are embeddings essential to the retrieval phase of a RAG system?

A. They represent semantic meaning.
B. They reduce model hallucination.
C. They compress the prompt length.
D. They provide a keyword index.

Answer: A

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.

NEW QUESTION # 71
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