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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
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: C

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 # 50
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

Answer: C

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.


NEW QUESTION # 51
What happens when the input provided to an LLM exceeds its context window limit?

Answer: C

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.


NEW QUESTION # 52
What is the role of embeddings in AI systems?

Answer: D


NEW QUESTION # 53
How does AI support exploratory data analysis when working with large, unstructured datasets?

Answer: C

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.


NEW QUESTION # 54
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