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Cisco 810-110 Exam Syllabus Topics:

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

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                                                  Cisco AI Technical Practitioner Sample Questions (Q51-Q56):

                                                  NEW QUESTION # 51
                                                  What is the purpose of a vector database in a RAG architecture?

                                                  Answer: C

                                                  Explanation:
                                                  A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.


                                                  NEW QUESTION # 52
                                                  In supervised machine learning, what is required?

                                                  Answer: C

                                                  Explanation:
                                                  Supervised learning trains models using labeled input-output pairs.


                                                  NEW QUESTION # 53
                                                  What is occurring when an agent converts a CSV list into a Markdown table for a final report?

                                                  Answer: C

                                                  Explanation:
                                                  Data transformation changes data from one format or structure into another, such as converting CSV rows into a Markdown table for reporting.


                                                  NEW QUESTION # 54
                                                  Which concept helps reduce overfitting in machine learning?

                                                  Answer: C

                                                  Explanation:
                                                  Regularization techniques help models generalize better and avoid memorizing training data.


                                                  NEW QUESTION # 55
                                                  Which metric is commonly used to evaluate classification models?

                                                  Answer: D

                                                  Explanation:
                                                  Accuracy measures how many predictions were correct out of total predictions.


                                                  NEW QUESTION # 56
                                                  ......

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