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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
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

NEW QUESTION # 21
What is a difference between generative AI and agentic AI?

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

Answer: D

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 # 22
What is a use case for generative AI in the testing phase of the software development lifecycle?

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

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 # 23
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

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

Answer: B

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 # 24
What is aggregation bias in the context of AI-assisted data analysis?

A. when AI uses summary statistics instead of raw data
B. when AI fails to combine conflicting patterns
C. when data is omitted due to incompatible formats
D. when a summary masks subpatterns

Answer: D

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.

NEW QUESTION # 25
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