810-110 Test Questions: Cisco AI Technical Practitioner & 810-110 Actual Test & 810-110 Exam Simulation

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
AI Ethics and Security- Security threats and mitigations
  • 1. Data privacy
  • 2. Model security
- Ethical considerations in AI
  • 1. Bias and fairness
  • 2. Transparency and explainability
AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
Prompt Engineering15%- Principles and patterns
  • 1. Roles
  • 2. Constraints
  • 3. Instructions
- Security and safety
  • 1. Defensive prompting methods
  • 2. Reducing hallucinations
  • 3. Improving response accuracy
  • 4. Prompt injection attacks
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
Agentic AI20%- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Data transformation and mapping
  • 1. Within AI Agents
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
Generative AI Models20%- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Common use cases
  • 1. Content creation
  • 2. Text summarization
  • 3. Code generation
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Vector databases
  • 3. Grounding AI outputs in external knowledge
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
Data Research and Analysis- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis

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Cisco AI Technical Practitioner Sample Questions (Q17-Q22):

NEW QUESTION # 17
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?

Answer: D

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.


NEW QUESTION # 18
Why are prompts important in generative AI systems?

Answer: C

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 19
What is a corporate data privacy risk when using a public AI chatbot?

Answer: D

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.


NEW QUESTION # 20
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

Answer: C

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.


NEW QUESTION # 21
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

Answer: B

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.


NEW QUESTION # 22
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