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Cisco 810-110 Exam Syllabus Topics:

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

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                                                  It is similar to the 810-110 desktop-based software, with all the elements of the desktop practice exam. This mock exam can be accessed from any browser and does not require installation. The Cisco AI Technical Practitioner (810-110) questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based Cisco AI Technical Practitioner (810-110) practice test immediately through any operating system and browsers.

                                                  Cisco AI Technical Practitioner Sample Questions (Q57-Q62):

                                                  NEW QUESTION # 57
                                                  What is the purpose of tokenization in Large Language Models (LLMs)?

                                                  Answer: B

                                                  Explanation:
                                                  Tokenization converts text into smaller units (tokens) that AI models can process.


                                                  NEW QUESTION # 58
                                                  Which Cisco technology is commonly associated with network observability and telemetry?

                                                  Answer: B

                                                  Explanation:
                                                  NetFlow provides visibility into traffic flows and is widely used for monitoring, analytics, and AI- driven operations.


                                                  NEW QUESTION # 59
                                                  Which approach protects PII when utilizing a public AI service for data analysis?

                                                  Answer: A

                                                  Explanation:
                                                  Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.


                                                  NEW QUESTION # 60
                                                  Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

                                                  Answer: C

                                                  Explanation:
                                                  Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.


                                                  NEW QUESTION # 61
                                                  Which activity is an agentic AI use case compared to a standard generative AI use case?

                                                  Answer: C

                                                  Explanation:
                                                  Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.


                                                  NEW QUESTION # 62
                                                  ......

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