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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Agentic AI Systems- Agent architecture
  • 1. Multi-step reasoning workflows
    • 2. Autonomous agents and orchestration
      Topic 2: AI for Data Research and Analysis- Data exploration and transformation
      • 1. Insight generation and validation
        • 2. AI-assisted data cleaning
          Topic 3: 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 4: Prompt Engineering15%- Prompt design principles
                    • 1. Roles, instructions, constraints
                      - Prompting techniques
                      • 1. Chained and iterative prompting
                        • 2. Few-shot prompting
                          - Security and robustness
                          • 1. Mitigation and defensive prompting
                            • 2. Prompt injection risks
                              Topic 5: Generative AI Models20%- Model hosting and architecture
                              • 1. Cloud vs local deployment trade-offs
                                • 2. Latency, cost, privacy considerations
                                  - Context and retrieval systems
                                  • 1. RAG, embeddings, vector databases
                                    • 2. Context windows and token limits
                                      - Model fundamentals and use cases
                                      • 1. Use cases: summarization, code generation, content creation
                                        • 2. LLMs and diffusion models overview
                                          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 (Q54-Q59):

                                                  NEW QUESTION # 54
                                                  A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

                                                  Answer: B

                                                  Explanation:
                                                  Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.


                                                  NEW QUESTION # 55
                                                  Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

                                                  Answer: C

                                                  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 # 56
                                                  In supervised machine learning, what is required?

                                                  Answer: B

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


                                                  NEW QUESTION # 57
                                                  Which AI concern focuses on protecting sensitive information?

                                                  Answer: A

                                                  Explanation:
                                                  AI systems must ensure sensitive data is securely handled and protected.


                                                  NEW QUESTION # 58
                                                  Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

                                                  Answer: A

                                                  Explanation:
                                                  Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.


                                                  NEW QUESTION # 59
                                                  ......

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