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Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: AI for Code and Workflow Optimization | 15% | - Integration of AI tools in development processes - Automation and workflow enhancement - AI-assisted code generation and improvement |
| Topic 3: 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 4: Agentic AI | 15% | - Agentic AI vs Generative AI use cases - Model Context Protocol (MCP) framework - Agent design principles and capabilities - Human-in-the-loop strategies |
| Topic 5: Prompt Engineering | 15% | - Advanced prompting techniques - Prompt design principles and best practices - Defensive prompting and error mitigation |
| Topic 6: Data Research and Analysis | 15% | - Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing |
Cisco AI Technical Practitioner Sample Questions:
Question 1
Which metric is commonly used to evaluate classification models?
A. Voltage
B. Packet TTL
C. Throughput only
D. Accuracy
Question 2
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?
A. It allows for easy repurposing.
B. It minimizes token consumption.
C. It maintains contextual alignment.
D. It ensures the most creative text.
Question 3
What is occurring when an agent converts a CSV list into a Markdown table for a final report?
A. data transformation
B. data filtering
C. data quantization
D. data classification
Question 4
In supervised machine learning, what is required?
A. Random hardware failures
B. Only encrypted traffic
C. No data
D. Labeled datasets
Question 5
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 orchestrates task delegation across specialized agents and aggregates the outputs.
C. It fine-tunes each agent's model weights to optimize performance on assigned tasks.
D. It replaces the need for multiple agents by handling tasks within a single reasoning loop.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: B |
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