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Cisco 810-110 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement |
| Prompt Engineering | 15% | - Advanced prompting techniques - Prompt design principles and best practices - Defensive prompting and error mitigation |
| Generative AI Models | 20% | - Model hosting options and trade-offs - Context windows, token limits and response management - Model selection and evaluation in AI hubs - Generative AI model families and use cases |
| Data Research and Analysis | 15% | - Data visualization and insight generation - Data quality, bias and validation - Data collection, preparation and preprocessing |
| 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 |
| Agentic AI | 15% | - Model Context Protocol (MCP) framework - Agent design principles and capabilities - Human-in-the-loop strategies - Agentic AI vs Generative AI use cases |
Cisco AI Technical Practitioner Sample Questions:
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?
- A. correlate
- B. hypothesize
- C. orient
- D. visualize
Correct Answer: B 🗳️
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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. chain-of-thought prompting
- B. few-shot prompting
- C. prompt chaining
- D. meta-prompting
Correct Answer: C 🗳️
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A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?
- A. to increase the model creativity
- B. to increase the response speed
- C. to verify the output reliability
- D. to bypass the context window
Correct Answer: C 🗳️
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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 automatically switching to a lower-cost model as the conversation history grows
- C. by capping the cumulative number of input tokens sent with each new prompt
- D. by increasing the context window size to reduce the total number of API calls
Correct Answer: C 🗳️
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What is a corporate data privacy risk when using a public AI chatbot?
- A. The data will be encrypted at rest instead of in transit.
- B. The AI will provide biased technical advice.
- C. The input data may be used for model training.
- D. The AI will provide hallucinated technical specifications.
Correct Answer: C 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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