ML Model Lifecycle And Operations

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

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

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

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?

Which TWO languages are natively supported for the Azure ML SDK?

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

You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?

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

You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?

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

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

You are debugging an Azure ML pipeline. You want to see the stdout of a specific step that failed. How do you access this?

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

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

What is the 'Workspace' in Azure Machine Learning?

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

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

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

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

You have an automated deployment pipeline. You want to run an integration test on the model after deployment. Which tool is best suited for this?

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

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?

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 orchestrating a multi-step ML pipeline in Azure Machine Learning. You need to ensure that a downstream step only executes if the upstream model training step finishes successfully, while allowing the pipeline to continue even if a non-critical logging step fails. Which configuration should you use?

A team uses Azure Machine Learning to track experiments. You need to ensure that every run is associated with a specific git commit hash to ensure reproducibility. Where should this be configured?

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?

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 configuring a 'Managed Online Endpoint' for a very large model (10GB+). The deployment is failing during the 'pulling image' phase. What is the most likely cause?

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

You are reviewing the 'Run History' in Azure Machine Learning. You want to compare the training time of two different experiments. Which UI feature should you use?

You are deploying a model to an Azure Kubernetes Service (AKS) cluster. You need to ensure that the deployment handles traffic spikes by scaling based on GPU usage. Which setting must be enabled in the inference configuration?

Which THREE settings are part of the 'InferenceConfig' object in Azure ML?

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

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 have an Azure Machine Learning pipeline that uses 'PipelineData' to pass information between steps. You want to share data between a training step and a scoring step. What is the recommended way to persist this data?

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

You are troubleshooting a model deployment failure where the container fails to start due to missing environment variables. Where do you find the logs to identify the cause?

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 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 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 are monitoring model drift. Which TWO features are required to configure a Data Drift Monitor?

You want to enforce a policy that all models must be registered before being deployed. Which feature should you use to implement this constraint?

You are configuring an 'Azure Machine Learning Compute Cluster' for a heavy training job. You want to ensure it shuts down automatically when no jobs are running. What setting should you configure?

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?

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 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?

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 managing model versioning in Azure Machine Learning Registry. You need to promote a model from 'Staging' to 'Production' without creating a new asset version. Which command or action should you perform?

You are using Azure Machine Learning Pipelines. Which THREE triggers can be used to start a pipeline?

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?

Which THREE types of information are found in a Run Object in Azure ML?

You need to ensure that your Azure ML models are deployed with high availability across multiple regions. Which deployment strategy should you configure?

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 'Managed Online Endpoint' with SSL termination. Where do you manage the SSL certificates?

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

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

A model is exhibiting data drift. You have created a drift monitor. What is the next step to automate the retraining?