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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
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
Generative AI Models20%- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Common use cases
  • 1. Content creation
  • 2. Code generation
  • 3. Text summarization
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Grounding AI outputs in external knowledge
  • 3. Vector databases
AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Prompt Engineering15%- Principles and patterns
  • 1. Instructions
  • 2. Roles
  • 3. Constraints
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Iterative prompting
  • 3. Chained prompting
- Security and safety
  • 1. Reducing hallucinations
  • 2. Defensive prompting methods
  • 3. Improving response accuracy
  • 4. Prompt injection attacks
AI Ethics and Security- Security threats and mitigations
  • 1. Data privacy
  • 2. Model security
- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
Agentic AI20%- 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. MCP framework primitives
  • 2. Context in agentic AI
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison

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Cisco AI Technical Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which role does AI play in assisting with the refactoring of software code?

Answer: B

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 # 43
What is a use case for generative AI in the testing phase of the software development lifecycle?

Answer: D

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.


NEW QUESTION # 44
How does AI support exploratory data analysis when working with large, unstructured datasets?

Answer: B

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.


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 must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: D

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 # 47
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