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Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Research and Analysis | 15% | - Data quality, bias and validation - Data visualization and insight generation - Data collection, preparation and preprocessing |
| 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 |
| Agentic AI | 15% | - Agent design principles and capabilities - Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases |
| AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - AI-assisted code generation and improvement - Automation and workflow enhancement |
| 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 - Model hosting options and trade-offs - Context windows, token limits and response management - Model selection and evaluation in AI hubs |
Cisco AI Technical Practitioner Sample Questions:
Which AI concern focuses on protecting sensitive information?
- A. Data privacy
- B. VLAN tagging
- C. Packet fragmentation
- D. Clock synchronization
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A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?
- A. few-shot prompting
- B. metaprompting
- C. zero-shot prompting
- D. chain-of-thought
Explanation: Only visible for PassSureExam members. You can sign-up / login (it's free).
Which concept helps reduce overfitting in machine learning?
- A. Increasing random noise only
- B. Regularization
- C. Disabling validation
- D. Ignoring datasets
Explanation: Only visible for PassSureExam members. You can sign-up / login (it's free).
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?
- A. using known patterns
- B. implementing constraints
- C. using smaller contexts
- D. removing system prompts
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What happens when the input provided to an LLM exceeds its context window limit?
- A. The model switches to a different algorithm.
- B. The model caches the overflow data.
- C. The model increases its context window size.
- D. The model loses the earliest part of the text.
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