Verified and Updated Cisco 810-110 Exam Questions & Answers

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prompt Engineering15%- Prompt structures
  • 1. Image generation
  • 2. Audio generation
  • 3. Text generation
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Chained prompting
  • 3. Iterative prompting
- Security and safety
  • 1. Defensive prompting methods
  • 2. Improving response accuracy
  • 3. Reducing hallucinations
  • 4. Prompt injection attacks
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
Topic 2: AI Ethics and Security- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
- Ethical considerations in AI
  • 1. Bias and fairness
  • 2. Transparency and explainability
Topic 3: Generative AI Models20%- Common use cases
  • 1. Text summarization
  • 2. Content creation
  • 3. Code generation
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Grounding AI outputs in external knowledge
  • 3. Vector databases
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Context windows and token management
  • 1. Token limits
  • 2. Response management
Topic 4: Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Topic 5: Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- Data transformation and mapping
  • 1. Within AI Agents
Topic 6: AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging

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Cisco AI Technical Practitioner Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: B

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 73
What is the purpose of a vector database in a RAG architecture?

Answer: B

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.


NEW QUESTION # 74
How does a coordinator agent function in a multi-agent AI framework?

Answer: D

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.


NEW QUESTION # 75
What is the role of embeddings in AI systems?

Answer: D

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.


NEW QUESTION # 76
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 # 77
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