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

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Cisco AI Technical Practitioner Sample Questions (Q41-Q46):

NEW QUESTION # 41
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. increasing the model temperature
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 # 42
How does context window management help control the cost of a long-running AI conversation?

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

Answer: C

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 # 43
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

A. stating the purpose
B. defining a persona
C. describing the situation
D. using few-shot

Answer: C

Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.

NEW QUESTION # 44
How does a coordinator agent function in a multi-agent AI framework?

A. It serves as the external data source that each agent queries for grounding information.
B. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
C. It orchestrates task delegation across specialized agents and aggregates the outputs.
D. It fine-tunes each agent's model weights to optimize performance on assigned tasks.

Answer: C

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.

NEW QUESTION # 45
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

A. generative adversarial networks
B. diffusion models
C. variational autoencoders
D. large language models

Answer: D

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.

NEW QUESTION # 46
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