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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ethics and Security15%- Data privacy and governance
  • 1. Secure AI data handling
    - Responsible AI principles
    • 1. Fairness, transparency, accountability
      • 2. Bias mitigation
        - AI security threats
        • 1. AI-specific attack surfaces
          • 2. Misinformation and hallucinations
            Topic 2: AI for Data Research and Analysis- Data exploration and transformation
            • 1. AI-assisted data cleaning
              • 2. Insight generation and validation
                Topic 3: AI for Code and Workflow Optimization- Automation workflows
                • 1. Workflow orchestration concepts
                  • 2. API integration basics
                    - AI-assisted software development
                    • 1. SDLC integration
                      • 2. Code generation and debugging
                        Topic 4: Agentic AI Systems- Agent architecture
                        • 1. Autonomous agents and orchestration
                          • 2. Multi-step reasoning workflows
                            Topic 5: Prompt Engineering15%- Prompt design principles
                            • 1. Roles, instructions, constraints
                              - Prompting techniques
                              • 1. Few-shot prompting
                                • 2. Chained and iterative prompting
                                  - Security and robustness
                                  • 1. Prompt injection risks
                                    • 2. Mitigation and defensive prompting
                                      Topic 6: Generative AI Models20%- Context and retrieval systems
                                      • 1. Context windows and token limits
                                        • 2. RAG, embeddings, vector databases
                                          - Model hosting and architecture
                                          • 1. Latency, cost, privacy considerations
                                            • 2. Cloud vs local deployment trade-offs
                                              - Model fundamentals and use cases
                                              • 1. Use cases: summarization, code generation, content creation
                                                • 2. LLMs and diffusion models overview

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                                                  Cisco AI Technical Practitioner Sample Questions (Q54-Q59):

                                                  NEW QUESTION # 54
                                                  How does context window management help control the cost of a long-running AI conversation?

                                                  Answer: A

                                                  Explanation:
                                                  Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.


                                                  NEW QUESTION # 55
                                                  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 # 56
                                                  Why are embeddings essential to the retrieval phase of a RAG system?

                                                  Answer: B

                                                  Explanation:
                                                  Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.


                                                  NEW QUESTION # 57
                                                  What is the role of the client in the Model Context Protocol?

                                                  Answer: B

                                                  Explanation:
                                                  In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.


                                                  NEW QUESTION # 58
                                                  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: C

                                                  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 # 59
                                                  ......

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