ML Model Lifecycle And Operations

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?

Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?

You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?

You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?

You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?

You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?

You are defining an Azure Machine Learning environment for a training job. The environment requires a specific set of Python libraries. What is the best practice for defining these dependencies?

You are moving a model from a local environment to Azure Machine Learning. Which file is required to define the entry script for the model inference?

You need to ensure that training data is encrypted at rest in the Blob Storage linked to your Azure Machine Learning workspace. How do you ensure this?

What is the primary purpose of a 'Labeling Project' in Azure Machine Learning?

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

What is the purpose of a 'Datastore' in Azure Machine Learning?

Which Azure Machine Learning resource provides a pre-configured environment for development?

You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?

Which service allows you to track ML models and their associated artifacts?

What is the 'Workspace' in Azure Machine Learning?

What is the benefit of using 'Azure Machine Learning Datasets' (or Data Assets) over raw storage paths?

You have an automated model training pipeline that is failing intermittently due to compute availability. What should you configure to ensure the pipeline is more resilient?

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

You need to ensure that your model inference code has access to the latest secret keys without hardcoding them. What is the recommended integration?

You are managing model lifecycle security. Which THREE actions are recommended to secure your ML models?

You are creating a 'Pipeline' and want to share a dataset across multiple steps. What is the most efficient way to access this data?

Which THREE components are required to define a 'Managed Online Deployment'?

Which TWO types of compute can be used for training in Azure Machine Learning?

You are configuring a 'Managed Online Endpoint' for production. You want to ensure high availability. What should you configure?

You are optimizing your Azure ML pipeline performance. Which THREE steps should you take to reduce execution time?

You are preparing a model for deployment. Which THREE items should you include in the model package?

Which THREE metrics can be logged during training to track performance in Azure ML?

Which TWO actions can you perform in the Azure Machine Learning studio?

You are troubleshooting a deployment. Which THREE logs are most helpful to check?

Which TWO resources are created inside an Azure Machine Learning workspace?

Which THREE security features are essential for a production Azure ML deployment?

Which THREE items are captured in the experiment lineage in Azure Machine Learning?

You are an MLOps engineer at a financial services company. Your team trains a fraud detection model weekly using an Azure Machine Learning pipeline. Compliance requires that every model registered in the workspace is traceable to the exact training run, dataset version, and code commit that produced it. You need to ensure this lineage is automatically captured for every registered model without manual documentation. What should you do?

You manage a managed online endpoint in Azure Machine Learning that serves a recommendation model. The model was trained months ago, and you now need to deploy a new version trained on recent data with zero downtime. You must be able to send a small percentage of live traffic to the new version to validate it before full rollout. What should you do?

You are an MLOps engineer preparing to retrain a demand forecasting model on a schedule. The training data resides in an Azure Machine Learning datastore backed by Azure Blob Storage. You need the training job to run automatically every night at 02:00 UTC without manual intervention. What should you create?

You are responsible for monitoring a deployed model on an Azure Machine Learning managed online endpoint. The model's input feature distribution has drifted from the training baseline, and you need to detect this automatically and act on it. Which two actions should you take? (Choose two.)