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

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Cisco AI Technical Practitioner Sample Questions (Q15-Q20):

NEW QUESTION # 15
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 # 16
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

Answer: A

Explanation:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.


NEW QUESTION # 17
Which Cisco AI capability helps detect abnormal network behavior?

Answer: C

Explanation:
AI systems can identify unusual patterns and potential threats automatically.


NEW QUESTION # 18
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: C

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 19
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 # 20
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