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Cisco 810-110 Exam Syllabus Topics:

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

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                                                  Cisco AI Technical Practitioner Sample Questions (Q25-Q30):

                                                  NEW QUESTION # 25
                                                  What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

                                                  Answer: B

                                                  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 # 26
                                                  Why might an AI practitioner use a model hub such as Hugging Face in the early phases of an AI project?

                                                  Answer: A

                                                  Explanation:
                                                  A model hub provides a centralized environment for discovering, examining, comparing, and obtaining pretrained artificial intelligence models. Option B is correct because platforms such as Hugging Face Hub enable practitioners to evaluate multiple model architectures before selecting a solution.
                                                  During the initial stages of an AI project, teams must determine which available models satisfy their functional requirements, infrastructure constraints, latency objectives, and deployment conditions.
                                                  A model hub can provide access to model documentation, architecture details, parameter counts, licensing information, evaluation results, and compatible deployment frameworks.
                                                  For example, a practitioner developing a technical document classifier can compare language models based on accuracy, model size, inference requirements, and licensing restrictions.
                                                  Option A is incorrect because model hubs may provide datasets and supporting tools, but their primary purpose is not automatically generating fine-tuned datasets.
                                                  Option C is incorrect because repository availability does not guarantee that a model is unbiased, secure, or suitable for deployment.
                                                  Option D is incorrect because API token expenditure monitoring requires appropriate billing and observability systems.
                                                  Model selection should include evaluation of provenance, licensing, security, task-specific performance, and infrastructure compatibility.
                                                  Specific Cisco AITECH Course Guide References:
                                                  * Generative AI Ecosystem
                                                  * AI Architect's Toolkit
                                                  * Customizing AI Models
                                                  * Exam Objective 1.4: Model selection in AI model hubs and repositories


                                                  NEW QUESTION # 27
                                                  What is hallucination in generative AI?

                                                  Answer: D

                                                  Explanation:
                                                  Hallucination occurs when an AI confidently produces false or misleading outputs.


                                                  NEW QUESTION # 28
                                                  A practitioner is using AI to analyze customer support tickets and usage data in order to compare complaint patterns across different customer segments. Following the 4-step EDA (Exploratory Data Analysis) methodology, what should be done immediately after producing a high-level summary of the feedback data?

                                                  Answer: C

                                                  Explanation:
                                                  Exploratory Data Analysis (EDA) is a systematic process for understanding datasets, identifying recurring patterns, investigating relationships, and developing evidence-based hypotheses. Option D is correct when following the four-step sequence Orient # Visualize # Correlate # Hypothesize.
                                                  The initial high-level summary represents the Orient stage, during which the practitioner establishes an overview of customer feedback, dominant complaint categories, and customer segments.
                                                  After completing this orientation, the next step is Visualize. During this stage, AI-assisted tools can generate bar charts, frequency distributions, segmented comparisons, and other visual representations that make underlying patterns easier to identify.
                                                  For example, a bar chart might reveal that enterprise customers report substantially more integration problems than small-business customers. Such observations establish a basis for subsequent analysis.
                                                  Option A is incorrect because orientation has already been completed through the high-level summary.
                                                  Option B is incorrect because correlation belongs to a later stage, where analysts investigate relationships across customer characteristics, complaint patterns, and usage metrics.
                                                  Option C is incorrect under the stated sequence because hypothesis generation follows exploration and correlation. Developing explanations prematurely can introduce confirmation bias.
                                                  The workflow helps analysts move from initial observations toward defensible, testable business hypotheses.
                                                  Specific Cisco AITECH Course Guide References:
                                                  * AI for Data Engineering and Exploration
                                                  * AI-Powered Discovery and Synthesis
                                                  * Exam Objective 4.1: AI's role in exploratory data analysis
                                                  Cisco's official blueprint confirms EDA coverage under Objective 4.1. The four-step ordering is supported by a separate published methodology description, not explicitly enumerated in the public Cisco blueprint.
                                                  Answer-key validation note: Several third-party AITECH question banks identify Hypothesize (C) as the answer to this question. That conflicts with the published four-stage sequence, in which Visualize (D) immediately follows Orient. Therefore, D is the methodologically consistent selection; the exact Cisco course- book key has not been independently established.


                                                  NEW QUESTION # 29
                                                  Which AI model type is best suited for image recognition tasks?

                                                  Answer: D

                                                  Explanation:
                                                  CNNs are optimized for image and visual pattern recognition.


                                                  NEW QUESTION # 30
                                                  ......

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