810-110 Exam Questions Dumps, Cisco AI Technical Practitioner VCE Collection

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Cisco 810-110 Exam Syllabus Topics:

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

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                                                  Cisco AI Technical Practitioner Sample Questions (Q37-Q42):

                                                  NEW QUESTION # 37
                                                  Which concept helps reduce overfitting in machine learning?

                                                  Answer: B

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


                                                  NEW QUESTION # 38
                                                  What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

                                                  Answer: C

                                                  Explanation:
                                                  A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.


                                                  NEW QUESTION # 39
                                                  What is the role of an MCP server in the MCP open standard?

                                                  Answer: D

                                                  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 # 40
                                                  In supervised machine learning, what is required?

                                                  Answer: C


                                                  NEW QUESTION # 41
                                                  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 # 42
                                                  ......

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