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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Agentic AI20%- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Data transformation and mapping
  • 1. Within AI Agents
- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
Topic 2: AI for Code and Workflow Optimization- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
- Workflow automation
  • 1. Task optimization
  • 2. Process automation
Topic 3: 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
Topic 4: 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
Topic 5: Prompt Engineering15%- Prompting techniques
  • 1. Iterative prompting
  • 2. Few-shot prompting
  • 3. Chained prompting
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Security and safety
  • 1. Reducing hallucinations
  • 2. Improving response accuracy
  • 3. Prompt injection attacks
  • 4. Defensive prompting methods
Topic 6: Generative AI Models20%- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- 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. Grounding AI outputs in external knowledge
  • 3. Embeddings
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations

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Cisco AI Technical Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

Answer: A

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.


NEW QUESTION # 11
What is a use case for generative AI in the testing phase of the software development lifecycle?

Answer: B

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 # 12
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: A

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 # 13
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

Answer: C

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.


NEW QUESTION # 14
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

Answer: D

Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.


NEW QUESTION # 15
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