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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

Answer: D

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.


NEW QUESTION # 43
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: C

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.


NEW QUESTION # 44
What is the role of the client in the Model Context Protocol?

Answer: A


NEW QUESTION # 45
Which AI model type is best suited for image recognition tasks?

Answer: A

Explanation:
CNNs are optimized for image and visual pattern recognition.


NEW QUESTION # 46
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

Answer: A

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.


NEW QUESTION # 47
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