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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

A. approval gate
B. injection mitigation
C. data lineage
D. drift monitoring

Answer: A

Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.

NEW QUESTION # 27
What is the purpose of a vector database in a RAG architecture?

A. to manage the context window
B. to perform model weight updates
C. to store numerical representations of data
D. to allocate resources for response generation

Answer: C

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.

NEW QUESTION # 28
Why is defining a persona considered a best practice for technical prompt engineering?

A. It provides a pattern to emulate.
B. It guides the tone and vocabulary.
C. It provides instructions for desired action.
D. It reduces the total token usage.

Answer: B

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.

NEW QUESTION # 29
Which Cisco AI capability helps detect abnormal network behavior?

A. Static routing
B. AI-driven anomaly detection
C. Manual packet counting
D. BIOS recovery mode

Answer: B

Explanation:
AI systems can identify unusual patterns and potential threats automatically.

NEW QUESTION # 30
How does context window management help control the cost of a long-running AI conversation?

A. by capping the cumulative number of input tokens sent with each new prompt
B. by increasing the context window size to reduce the total number of API calls
C. by automatically switching to a lower-cost model as the conversation history grows
D. by switching to an algorithm that reduces the character-to-token ratio

Answer: A

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.

NEW QUESTION # 31
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