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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering15%- Security and safety
  • 1. Prompt injection attacks
  • 2. Reducing hallucinations
  • 3. Improving response accuracy
  • 4. Defensive prompting methods
- Prompt structures
  • 1. Text generation
  • 2. Audio generation
  • 3. Image generation
- Principles and patterns
  • 1. Constraints
  • 2. Roles
  • 3. Instructions
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Iterative prompting
  • 3. Chained prompting
AI for Code and Workflow Optimization- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
- Workflow automation
  • 1. Task optimization
  • 2. Process automation
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
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
Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- 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
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
Generative AI Models20%- Common use cases
  • 1. Content creation
  • 2. Text summarization
  • 3. Code generation
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Grounding AI outputs in external knowledge
  • 3. Embeddings
- 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
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)

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Cisco AI Technical Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
What is the role of an MCP server in the MCP open standard?

Answer: D

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 # 44
Which action demonstrates the principle of fairness in a generative AI deployment?

Answer: B

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.


NEW QUESTION # 45
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

Answer: C

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.


NEW QUESTION # 46
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: A

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 # 47
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: C

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.


NEW QUESTION # 48
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