AI-300 Zertifizierungsfragen, Microsoft AI-300 PrüfungFragen

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Design and implement a GenAIOps infrastructure20–25%- Implement infrastructure for generative AI workloads
  • 1. Design scalable and secure architecture
    • 2. Integrate with Azure services and tools
      - Set up Microsoft Foundry environment
      • 1. Configure projects, connections, and security
        • 2. Manage compute and deployment resources
          Design and implement an MLOps infrastructure15–20%- Implement infrastructure as code for Machine Learning
          • 1. Automate infrastructure provisioning
            • 2. Use Bicep or Azure CLI to deploy resources
              - Create and manage Machine Learning workspace resources and assets
              • 1. Manage compute targets, datastores, and environments
                • 2. Configure workspace settings and security
                  Optimize generative AI systems and model performance15–20%- Optimize model selection and configuration
                  • 1. Choose appropriate models and parameters
                    • 2. Tune prompts and generation settings
                      - Improve efficiency and cost-effectiveness
                      • 1. Manage resource utilization
                        • 2. Optimize inference and deployment
                          Implement machine learning model lifecycle and operations25–30%- Monitor and maintain models in production
                          • 1. Monitor data and model drift
                            • 2. Implement retraining and update workflows
                              - Deploy models to production
                              • 1. Configure deployment options and scaling
                                • 2. Deploy to real-time and batch endpoints
                                  - Orchestrate model training and experimentation
                                  • 1. Track experiments and metrics
                                    • 2. Create and manage pipelines
                                      - Register, version, and package models
                                      • 1. Create reusable model packages
                                        • 2. Manage model registry
                                          Implement generative AI quality assurance and observability10–15%- Monitor generative AI systems
                                          • 1. Implement logging and alerting
                                            • 2. Track usage, performance, and errors
                                              - Evaluate and test generative AI applications
                                              • 1. Define evaluation metrics and criteria
                                                • 2. Test for safety, accuracy, and relevance

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Prüfungsfragen mit Lösungen (Q149-Q154):

                                                  149. Frage
                                                  Drag and Drop Question
                                                  You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
                                                  The deployment process must ensure that proper validation and control is maintained.
                                                  You need to promote the fine-tuned model from development to production.
                                                  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

                                                  Antwort:

                                                  Begründung:


                                                  150. Frage
                                                  You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
                                                  There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
                                                  You need to exclude the 20 files. What should you do?

                                                  Antwort: A


                                                  151. Frage
                                                  You manage an Azure Machine learning workspace named workspace1.
                                                  You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
                                                  You need to add the instantiated compute instance to workspace 1.
                                                  What should you use?

                                                  Antwort: D


                                                  152. Frage
                                                  A product team is building a customer support assistant that must respond consistently across multiple channels.
                                                  Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
                                                  The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
                                                  You need to design prompts that improve response quality while remaining flexible for future changes.
                                                  Which two actions should you perform? Each correct answer presents part of the solution.
                                                  (Choose two.)
                                                  NOTE: Each correct selection is worth one point.

                                                  Antwort: A,E

                                                  Begründung:
                                                  To achieve reliable, reusable, and adaptable prompts without retraining the model, you should implement Prompt Flow, use System Messages for tone/role definition, and enforce Output Parsing. These techniques standardize how the model behaves across all channels.
                                                  Here is the most effective approach to stabilize your customer support assistant:
                                                  [B]
                                                  1. Implement Prompt Flow
                                                  Instead of hardcoding raw prompts in your code, use Azure Machine Learning Prompt Flow.
                                                  Visual Workflows: Create executable flows that link LLMs, prompts, and Python tools.
                                                  Versioning: Easily track, iterate, and roll back prompt versions without touching application code.
                                                  Evaluation: Test prompt variants systematically against a set of baseline queries to objectively measure factual accuracy and tone before pushing to production.
                                                  [C]
                                                  2. Standardize System Messages
                                                  A well-defined system message serves as the "guardrails" for your assistant. It should dictate the persona, scope of knowledge, and safety rules.
                                                  Define the Persona: Instruct the model exactly how to behave (e.g., "You are a helpful, empathetic, and strictly technical support assistant for [Company Name].").Strict Guidelines:
                                                  Provide instructions on how to handle out-of-scope queries (e.g., "If you do not know the answer, do not guess. Apologize and route the user to [Support Email].").
                                                  Reference:
                                                  https://whitebeardstrategies.com/blog/5-best-practices-for-efficient-language-model-prompting/


                                                  153. Frage
                                                  You manage a Microsoft Foundry project. You build a multi-turn chatbot application.
                                                  You plan to filter your traces to identify issues while observing how the application is responding.
                                                  The solution must not use an external knowledge base.
                                                  You need to select an evaluation metric.
                                                  Which built-in evaluator should you use?

                                                  Antwort: B

                                                  Begründung:
                                                  In a Microsoft Foundry project for a multi-turn chatbot, the best built-in evaluator to use-- especially when you want to avoid an external knowledge base--is the Coherence evaluator or a combination of Agentic Evaluators.
                                                  Because your application is multi-turn, you need to observe how the AI maintains a logical flow and resolves user intent over several exchanges. Since you specifically want to avoid an external knowledge base (eliminating "Groundedness" or "Retrieval" metrics), you should focus on quality metrics that only require the conversation history itself.
                                                  Reference:
                                                  https://www.cekura.ai/blogs/why-single-turn-testing-falls-short-in-evaluating-conversational-ai


                                                  154. Frage
                                                  ......

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