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Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Prompt Engineering | 15% | - Advanced prompting techniques - Prompt design principles and best practices - Defensive prompting and error mitigation |
| Topic 3: Data Research and Analysis | 15% | - Data collection, preparation and preprocessing - Data visualization and insight generation - Data quality, bias and validation |
| Topic 4: Agentic AI | 15% | - Human-in-the-loop strategies - Agent design principles and capabilities - Model Context Protocol (MCP) framework - Agentic AI vs Generative AI use cases |
| Topic 5: AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - AI-assisted code generation and improvement - Automation and workflow enhancement |
| Topic 6: 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 |
Cisco AI Technical Practitioner Sample Questions:
Question 1
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?
A. to ensure the validity of AI decisions
B. to protect sensitive customer data
C. to prevent unauthorized access to data
D. to increase the speed of AI response
Question 2
Which role does AI play in assisting with the refactoring of software code?
A. reducing manual code documentation
B. creating a new database schema
C. identifying flaws and improving design
D. integrating a new API
Question 3
What is aggregation bias in the context of AI-assisted data analysis?
A. when a summary masks subpatterns
B. when AI fails to combine conflicting patterns
C. when data is omitted due to incompatible formats
D. when AI uses summary statistics instead of raw data
Question 4
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?
A. diffusion models
B. generative adversarial networks
C. variational autoencoders
D. large language models
Question 5
In supervised machine learning, what is required?
A. Random hardware failures
B. Only encrypted traffic
C. No data
D. Labeled datasets
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: D |
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