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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
AI for Code and Workflow Optimization- AI-assisted software development
  • 1. SDLC integration
    • 2. Code generation and debugging
      - Automation workflows
      • 1. Workflow orchestration concepts
        • 2. API integration basics
          Ethics and Security15%- Data privacy and governance
          • 1. Secure AI data handling
            - AI security threats
            • 1. Misinformation and hallucinations
              • 2. AI-specific attack surfaces
                - Responsible AI principles
                • 1. Fairness, transparency, accountability
                  • 2. Bias mitigation
                    AI for Data Research and Analysis- Data exploration and transformation
                    • 1. Insight generation and validation
                      • 2. AI-assisted data cleaning
                        Generative AI Models20%- Model hosting and architecture
                        • 1. Latency, cost, privacy considerations
                          • 2. Cloud vs local deployment trade-offs
                            - Model fundamentals and use cases
                            • 1. LLMs and diffusion models overview
                              • 2. Use cases: summarization, code generation, content creation
                                - Context and retrieval systems
                                • 1. RAG, embeddings, vector databases
                                  • 2. Context windows and token limits
                                    Prompt Engineering15%- Prompt design principles
                                    • 1. Roles, instructions, constraints
                                      - Security and robustness
                                      • 1. Prompt injection risks
                                        • 2. Mitigation and defensive prompting
                                          - Prompting techniques
                                          • 1. Chained and iterative prompting
                                            • 2. Few-shot prompting
                                              Agentic AI Systems- Agent architecture
                                              • 1. Autonomous agents and orchestration
                                                • 2. Multi-step reasoning workflows

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                                                  Cisco AI Technical Practitioner Sample Questions (Q35-Q40):

                                                  NEW QUESTION # 35
                                                  Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

                                                  Answer: B

                                                  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 # 36
                                                  Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

                                                  Answer: D

                                                  Explanation:
                                                  Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.


                                                  NEW QUESTION # 37
                                                  A team uses AI to scaffold a new microservice. What does this process involve?

                                                  Answer: C

                                                  Explanation:
                                                  Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.


                                                  NEW QUESTION # 38
                                                  What is the role of an MCP server in the MCP open standard?

                                                  Answer: A

                                                  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 # 39
                                                  Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?

                                                  Answer: A

                                                  Explanation:
                                                  A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.


                                                  NEW QUESTION # 40
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