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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

Answer: B

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.


NEW QUESTION # 40
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

Answer: A

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.


NEW QUESTION # 41
What is the role of embeddings in AI systems?

Answer: A

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


NEW QUESTION # 42
What is the role of the client in the Model Context Protocol?

Answer: A

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.


NEW QUESTION # 43
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: A

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 # 44
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