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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

Answer: D

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 # 18
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

Answer: B

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.


NEW QUESTION # 19
Why is defining a persona considered a best practice for technical prompt engineering?

Answer: C

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.


NEW QUESTION # 20
Which role does AI play in assisting with the refactoring of software code?

Answer: A

Explanation:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.


NEW QUESTION # 21
What is occurring when an agent converts a CSV list into a Markdown table for a final report?

Answer: D

Explanation:
Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.


NEW QUESTION # 22
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