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 on the review screen.You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.You plan to run the script.py Python script as a command job that trains a machine learning model.You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.Solution: python script.py --trainingdata ${{inputs.training_data}}Does the solution meet the goal?
Answer(s): A
Correct: * python script.py --training_data ${{inputs.training_data}} The scipt is named script.py. For the parameter use ${{inputs.training_data}}Incorrect: * python script.py --training_data dataset1.csv* python script.py dataset1.csv * python train.py --training_data training_dataNote: Read a TabularDataset, Example In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:* Details omitted* job = command( code="./src", # Local path where the code is stored *-> command="python train.py --inputs ${{inputs.input_data}}", inputs=my_job_inputs, environment="<environment_name>:<version>", compute="cpu-cluster", )
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2
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 on the review screen.You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.You plan to run the script.py Python script as a command job that trains a machine learning model.You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.Solution: python script.py dataset1.csvDoes the solution meet the goal?
Answer(s): B
Correct: * python script.py --training_data ${{inputs.training_data}} The scipt is named script.py. For the parameter use ${{inputs.training_data}}Incorrect: * python script.py --training_data dataset1.csv * python script.py dataset1.csv * python train.py --training_data training_dataNote: Read a TabularDataset, ExampleIn the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:* Details omitted* job = command( code="./src", # Local path where the code is stored *-> command="python train.py --inputs ${{inputs.input_data}}", inputs=my_job_inputs, environment="<environment_name>:<version>", compute="cpu-cluster", )
HOTSPOT (Drag and Drop is not supported)You review the following Azure CLI command and the relevant Bicep excerpt.(Non-relevant sections are omitted.)You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: Yes Yes- The command deploys the resources into an existing resource group named rg-foundry-dev.Box 2: No No- The system-assigned managed identity defined in the template will automatically be inherited by all Microsoft Foundry projects.In Azure, system-assigned managed identities are strictly bound to the individual resource on which they are enabled. They are not inherited by downstream or associated applications (like Microsoft Foundry projects) in the deployment hierarchy.Box 3: No No - To deploy the template to a different subscription, you must modify the Bicep file to include a subscriptionID parameter.A Bicep file does not strictly need to include a subscriptionId parameter to deploy to a different subscription.How you handle the cross-subscription deployment depends entirely on whether you are shifting the entire deployment context using external tooling, or using a multi-scope deployment directly within your code.Option 1: Handle via External Tooling (No Code Changes)If your entire Bicep file is designed to deploy to a single subscription, you do not need to alter your Bicep code. Instead, you change the subscription context externally before running the deployment command. The deployment tool will automatically route everything to the active subscription context.Option 2: Handle via Bicep Modules (Multi-Scope Deployment) If you need a single Bicep deployment to create resources across multiple subscriptions simultaneously, you must use Bicep modules. In this scenario, you do use a subscriptionId parameter or string inside the Bicep template to explicitly route the module to the target scope.You use the subscription() scope function to pass the targeted subscription ID directly to the module definition.
https://cloudtips.nl/the-magic-of-azure-managed-identities-%EF%B8%8F-19747c37e652 https://learn.microsoft.com/en-us/azure/devops/pipelines/tasks/reference/azure-resource-manager-template-deployment-v3
DRAG DROP (Drag and Drop is not supported)A team performs interactive experimentation during development. The team also runs scalable jobs for model training.The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.You need to configure compute targets that support each workload.Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.Note: Each correct selection is worth one point.Select and Place:
Box 1: Azure Machine Learning Compute Instance Interactive experimentationThe best compute target for interactive experimentation while maintaining low costs and allowing the flexibility to scale is an Azure Machine Learning Compute Instance.Optimized for Interactive Development: It acts as a managed, cloud-based data science workstation integrated directly with Jupyter Notebooks, JupyterLab, and VS Code.Cost Controls: It includes built-in cost-saving features like idle shutdown and automated stop/start scheduling. This ensures you only pay for compute hours when the environment is actively being used.Box 2: Azure Databricks cluster Scalable training jobsThe best compute target for scalable model training jobs in this scenario is an Azure Databricks cluster.Workload Optimization: Azure Databricks is explicitly optimized for data preparation and large-scale, distributed machine learning model training workloads (such as Apache Spark MLlib or distributed deep learning).Workload Diversity: Attaching an Azure Databricks cluster as a compute target allows you to handle specific heavy-duty engineering and training workloads while seamlessly transitioning back to your Azure Machine Learning Compute Instance for core interactive development.Incorrect: Azure Kubernetes Service In Azure Machine Learning architectures, Azure Kubernetes Service (AKS) is primarily designated and optimized as an inference compute target for hosting, scaling, and deploying trained models as real-time production endpoints. It is not the standard choice for general model training pipelines.
https://docs.azure.cn/en-us/machine-learning/concept-compute-target
DRAG DROP (Drag and Drop is not supported)You have an existing GitHub repository containing Azure Machine Learning project files.You need to clone the repository to your Azure Machine Learning shared workspace file system.Which four 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.Note: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.Select and Place:
Step 1: From the terminal window in the Azure Machine Learning interface, run the the ssh-keygen command.1. Generate an SSH key pair: ssh-keygen -t ed25519 -C "your_email@example.com"Step 2: From the terminal window in the Azure Machine Learning interface, run the cat ~/.ssh/id_rsa.pub command. Retrieve your public key: 2.. Display your public key so you can copy it to your GitHub account settings: cat ~/.ssh/id_ed25519.pubStep 3: Add a public key to the GitHub Account. Key Type to Add to GitHub 3. You must add the SSH public key (copied from the id_ed25519.pub file in step 2 above) to your GitHub account under Settings > SSH and GPG keys.Step 4: From the terminal window in the Azure Machine Learning interface, run the git clone command.Clone the repository: After adding the key to GitHub, navigate to your workspace folder and clone the repository using its SSH URL:4. Clone the repository using the SSH URL: git clone git@github.com:username/repository-name.git
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.A new version of the Docker image is available.You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.What should you do?
Answer(s): C
To use a new version of the Docker image for an environment using the Azure Machine Learning SDK v2, you must instantiate an Environment class object with the new image parameter and then use the ml_client.environments.create_or_update() method.Required Steps 1. Define the updated Environment: Use the Environment entity from the azure.ai.ml.entities package. Set the image parameter to the URI of the new Docker image version.2. Register or update the asset: Pass the environment instance into ml_client.environments.create_or_update () to create a new version of that environment asset within your workspace.
https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment-with-private-packages/environment-with-private-package-docker-image.ipynb
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?
Answer(s): D
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)
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2
A company has multiple data science teams working on separate machine learning projects.The company requires models to be auditable, reusable, and governed centrally across teams. The models must allow team-level isolation for billing.You need to establish the foundation for governed machine learning operations.Which action should you perform first?
The ideal first step is to implement a Hub-and-Spoke Workspace Architecture combined with Azure Machine Learning Registries. This hybrid blueprint natively decouples your organizational boundaries (billing and access) from your machine learning assets (models, environments, and pipelines).In enterprise-scale machine learning, an Azure Machine Learning Hub Workspace acts as a central governance plane. It lets IT administrators configure security, shared compute resources, networks, and compliance guidelines centrally once. From this central hub, different teams can spin up isolated project workspaces. This provides strict data and team-level separation, which naturally supports precise cost tracking and billing isolation per team.
https://learn.microsoft.com/en-us/azure/machine-learning/concept-hub-workspace
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