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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
AI for Code and Workflow Optimization	15%	- AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement
Data Research and Analysis	15%	- Data collection, preparation and preprocessing - Data quality, bias and validation - Data visualization and insight generation
Agentic AI	15%	- Agentic AI vs Generative AI use cases - Human-in-the-loop strategies - Model Context Protocol (MCP) framework - Agent design principles and capabilities
Generative AI Models	20%	- Model hosting options and trade-offs - Context windows, token limits and response management - Generative AI model families and use cases - Model selection and evaluation in AI hubs
AI Ethics and Security	20%	- Data privacy and security in AI systems - Compliance and governance frameworks - AI-specific threats, risks and misinformation - Responsible AI principles
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 (Q41-Q46):

NEW QUESTION # 41
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

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

Answer: C

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 # 42
What is a use case for generative AI in the testing phase of the software development lifecycle?

A. generating synthetic test data
B. generating automated documentation
C. drafting user stories
D. performing static code analysis

Answer: A

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.

NEW QUESTION # 43
Which protocol is commonly used for streaming telemetry in modern networks?

A. FTP
B. Telnet
C. ARP
D. gRPC

Answer: D

Explanation:
gRPC supports efficient real-time telemetry streaming in modern network automation environments.

NEW QUESTION # 44
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

A. parameter count of the model
B. release frequency of the model
C. response creativity level of the model
D. training duration of the model

Answer: A

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.

NEW QUESTION # 45
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. automated unit testing
B. code scaffolding
C. AI-assisted debugging
D. continuous integration optimization

Answer: C

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 # 46
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