AI-300 Prüfung - AI-300 Praxisprüfung

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Microsoft AI-300 Exam Syllabus Topics:

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

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Prüfungsfragen mit Lösungen (Q182-Q187):

                                                  182. Frage
                                                  You create an Azure Machine Learning workspace
                                                  You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
                                                  You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
                                                  How should you complete ten code? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point

                                                  Antwort:

                                                  Begründung:

                                                  Explanation:


                                                  183. Frage
                                                  A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
                                                  The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
                                                  You need to create a controlled evaluation of input data.
                                                  Which action should you perform first?

                                                  Antwort: A

                                                  Begründung:
                                                  In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
                                                  1. Generate Synthetic Interaction Data
                                                  Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
                                                  2. Compare Prompt Variants
                                                  Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
                                                  3. Controlled Evaluation
                                                  To measure performance objectively, run automated Eval Runs.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow


                                                  184. Frage
                                                  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
                                                  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
                                                  You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
                                                  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
                                                  You need to perform the task that must be completed before you can add the new kernel.
                                                  Solution: Create an environment.
                                                  Does the solution meet the goal?

                                                  Antwort: B

                                                  Begründung:
                                                  Correct:
                                                  * Create an environment.
                                                  Incorrect:
                                                  * Delete the Python 3.6 - AzureML kernel.
                                                  * Delete the Python 3.8 - AzureML kernel.
                                                  Note:
                                                  Before you can add a new Jupyter kernel on an Azure Machine Learning compute instance terminal, you must create a Conda environment.
                                                  Required Workflow
                                                  To officially provision and expose the new kernel to your Azure Machine Learning studio Notebooks, you need to execute the following full process from your terminal session:
                                                  Create the environment: Provision a new isolated environment (e.g., using conda create -n newenv python=3.10).
                                                  Activate the environment: Run conda activate newenv.
                                                  Install dependencies: Add the required ipykernel package using conda install ipykernel or pip install ipykernel.
                                                  Register the kernel: Bind the new environment configuration to the global Jupyter directory by running:
                                                  python -m ipykernel install --user --name newenv --display-name "My New Kernel" Reference:
                                                  https://docs.azure.cn/en-us/machine-learning/how-to-access-terminal


                                                  185. Frage
                                                  You manage an Azure Machine Learning workspace.
                                                  You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
                                                  Which parameter should you use?

                                                  Antwort: B

                                                  Begründung:
                                                  The appropriate parameter to use is image.
                                                  When defining a custom environment from an existing Docker image using the Environment class in the Azure Machine Learning Python SDK v2, you pass the Docker image registry URI directly to the image parameter.
                                                  Python SDK v2 Example
                                                  from azure.ai.ml.entities import Environment
                                                  # Define the environment using the 'image' parameter
                                                  env_docker_image = Environment(
                                                  image="pytorch/pytorch:latest", # <--- Appropriate parameter
                                                  name="docker-image-example",
                                                  description="Environment created from a Docker image."
                                                  )
                                                  # Register or update the environment in your workspace
                                                  ml_client.environments.create_or_update(env_docker_image)
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2


                                                  186. Frage
                                                  Hotspot Question
                                                  You have an Azure Machine Learning workspace named Workspace1.
                                                  You plan to train an image classification model by using Automated ML in Workspace1.
                                                  You need to complete the provided Azure Machine Learning Python SDK v2 code to bring labeled image data as input for model training.
                                                  How should you complete the code? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Antwort:

                                                  Begründung:

                                                  Explanation:
                                                  Box 1: azure.ai.ml.constants
                                                  azure.ai.ml.constants is the official SDK v2 submodule where the AssetType enum resides.
                                                  Box 2: MLTABLE
                                                  AssetType.MLTABLE: Automated ML for Computer Vision tasks (such as image classification and object detection) specifically requires your input data and corresponding label annotations to be provided via an MLTable asset type. This structure points to a folder containing your dataset configurations and your .jsonl bounding box or classification files.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models


                                                  187. Frage
                                                  ......

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