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Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Research and Analysis | 15% | - Data collection, preparation and preprocessing - Data visualization and insight generation - Data quality, bias and validation |
| Agentic AI | 15% | - Agent design principles and capabilities - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework |
| AI Ethics and Security | 20% | - AI-specific threats, risks and misinformation - Responsible AI principles - Data privacy and security in AI systems - Compliance and governance frameworks |
| AI for Code and Workflow Optimization | 15% | - Automation and workflow enhancement - Integration of AI tools in development processes - AI-assisted code generation and improvement |
| Prompt Engineering | 15% | - Advanced prompting techniques - Defensive prompting and error mitigation - Prompt design principles and best practices |
| Generative AI Models | 20% | - Generative AI model families and use cases - Context windows, token limits and response management - Model hosting options and trade-offs - Model selection and evaluation in AI hubs |
Cisco AI Technical Practitioner Sample Questions:
1. What is the role of embeddings in AI systems?
A) Representing data numerically in vector space
B) Assigning IP addresses
C) Compressing logs
D) Encrypting traffic
2. A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?
A) bias mitigation
B) inclusiveness
C) system safety
D) transparency
3. What is a difference between generative AI and agentic AI?
A) Generative AI produces single-turn responses and agentic AI executes multi-step reasoning loops.
B) Generative AI requires tool access and agentic AI relies on its training data.
C) Generative AI uses retrieval-augmented generation and agentic AI does not support external data sources.
D) Generative AI operates autonomously and agentic AI requires a prompt for each action.
4. A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?
A) meta-prompting
B) chain-of-thought prompting
C) few-shot prompting
D) prompt chaining
5. A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?
A) increasing the model temperature
B) minimizing the input prompt size
C) expanding the total context window
D) increasing the model sampling rate
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
