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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which metric is commonly used to evaluate classification models?

Answer: A

Explanation:
Accuracy measures how many predictions were correct out of total predictions.


NEW QUESTION # 64
Which protocol is commonly used for streaming telemetry in modern networks?

Answer: D

Explanation:
gRPC supports efficient real-time telemetry streaming in modern network automation environments.


NEW QUESTION # 65
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 # 66
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

Answer: C

Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.


NEW QUESTION # 67
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

Answer: A

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


NEW QUESTION # 68
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