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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering15%- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Iterative prompting
  • 3. Chained prompting
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Security and safety
  • 1. Defensive prompting methods
  • 2. Improving response accuracy
  • 3. Prompt injection attacks
  • 4. Reducing hallucinations
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. MCP framework primitives
  • 2. Context in agentic AI
- Data transformation and mapping
  • 1. Within AI Agents
Generative AI Models20%- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Vector databases
  • 3. Embeddings
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
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
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

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Cisco AI Technical Practitioner Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which AI concern focuses on protecting sensitive information?

Answer: B

Explanation:
AI systems must ensure sensitive data is securely handled and protected.


NEW QUESTION # 32
Which protocol is commonly used for streaming telemetry in modern networks?

Answer: A

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


NEW QUESTION # 33
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

Answer: C

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.


NEW QUESTION # 34
What is the role of an MCP server in the MCP open standard?

Answer: B

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.


NEW QUESTION # 35
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

Answer: D

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 # 36
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