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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
AI for Data Research and Analysis- Data exploration and transformation
  • 1. AI-assisted data cleaning
    • 2. Insight generation and validation
      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. LLMs and diffusion models overview
                • 2. Use cases: summarization, code generation, content creation
                  Agentic AI Systems- Agent architecture
                  • 1. Autonomous agents and orchestration
                    • 2. Multi-step reasoning workflows
                      Ethics and Security15%- Responsible AI principles
                      • 1. Bias mitigation
                        • 2. Fairness, transparency, accountability
                          - Data privacy and governance
                          • 1. Secure AI data handling
                            - AI security threats
                            • 1. Misinformation and hallucinations
                              • 2. AI-specific attack surfaces
                                Prompt Engineering15%- Security and robustness
                                • 1. Prompt injection risks
                                  • 2. Mitigation and defensive prompting
                                    - Prompting techniques
                                    • 1. Few-shot prompting
                                      • 2. Chained and iterative prompting
                                        - Prompt design principles
                                        • 1. Roles, instructions, constraints
                                          AI for Code and Workflow Optimization- Automation workflows
                                          • 1. API integration basics
                                            • 2. Workflow orchestration concepts
                                              - AI-assisted software development
                                              • 1. Code generation and debugging
                                                • 2. SDLC integration

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                                                  Cisco AI Technical Practitioner Sample Questions (Q43-Q48):

                                                  NEW QUESTION # 43
                                                  What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

                                                  Answer: C

                                                  Explanation:
                                                  Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


                                                  NEW QUESTION # 44
                                                  Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

                                                  Answer: A

                                                  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 # 45
                                                  Which AI concern focuses on protecting sensitive information?

                                                  Answer: B

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


                                                  NEW QUESTION # 46
                                                  A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

                                                  Answer: C

                                                  Explanation:
                                                  In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.


                                                  NEW QUESTION # 47
                                                  What is fine-tuning in generative AI?

                                                  Answer: A

                                                  Explanation:
                                                  Fine-tuning adapts a general AI model to specific domains or business use cases.


                                                  NEW QUESTION # 48
                                                  ......

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