Mlops Infrastructure

A data scientist needs to access data in an Azure Data Lake Storage Gen2 account from Azure ML. You need to ensure the workspace uses the most secure method to access this data. What should you configure?

You want to automate the deployment of a model using GitHub Actions. Which file format is standard for defining the Azure ML CLI v2 deployment configuration?

Your Azure ML training job fails with an 'Out of Memory' error on a GPU cluster. You need to ensure the job runs successfully without modifying the model code. What should you do?

You are creating a CI/CD pipeline in Azure DevOps. Which extension is essential to integrate Azure ML tasks into your build/release pipeline?

You are setting up an Azure Machine Learning workspace and need to ensure that the workspace is not accessible from the public internet. Which networking feature should you configure?

You need to monitor the health of your deployed Azure ML models. Which service is integrated directly with Azure ML to provide automated metrics and logging?

Which component in an Azure ML pipeline definition is responsible for specifying the runtime environment, including Python packages and Conda dependencies?

What is the primary function of an Azure ML 'Datastore'?

Which Azure feature is used to define infrastructure as code for your Azure ML workspace?

You are managing a multi-workspace environment. You need to restrict the ability to create new compute clusters to only specific users. Where should you apply this control?

You need to ensure that a training script running on Azure ML has access to secrets (like API keys) without hardcoding them in the script. What should you use?

Your team uses a custom Docker image for training. You need to update the image in the Azure Container Registry (ACR). What is the recommended workflow to ensure Azure ML picks up the latest version?

When configuring a compute instance, which setting allows you to automatically stop the instance during periods of inactivity?

You are deploying a model as a real-time endpoint. You want to ensure the deployment can handle sudden spikes in traffic. Which scaling configuration should you enable?

You want to track your model experiments and compare their performance metrics. Which Azure ML feature provides this capability?

In Azure ML, what is a 'Component'?

You need to debug a training job that is failing inside a specific Docker container. What is the best way to investigate?

What is the primary benefit of using Azure ML compute clusters instead of compute instances for training?

You are experiencing latency issues with a model deployed on a Managed Online Endpoint. What is the first thing you should check?

You need to orchestrate a series of ML steps (Data prep, Training, Evaluation). Which Azure ML feature is specifically designed for this?

You need to prevent data egress from your Azure ML environment. What configuration is required?

What is the purpose of the Azure ML 'Model Registry'?

You are using GitHub Actions. You want to authenticate to Azure without using hardcoded credentials. What should you use?

A pipeline step fails because it cannot find a file in the datastore. What is the most likely cause?

Which TWO of the following are benefits of using the Azure ML CLI v2 for your MLOps pipelines?

Which THREE of the following are key components of a robust MLOps strategy in Azure?

Which TWO of the following storage types can be registered as an Azure ML Datastore?

Which TWO of the following are required to secure an Azure ML workspace using a Private Link?

Which THREE of the following are valid compute targets for Azure Machine Learning training?

Which THREE factors influence the cost of an Azure ML Compute Cluster?

Which THREE of the following are types of Azure ML endpoints?

Which TWO of the following can be used to manage Azure ML assets like environments and models?

Which TWO of the following are benefits of using Azure ML Environments?

Which THREE of the following represent lifecycle stages of an ML model in Azure ML?

Which THREE of the following are common reasons for a model deployment to fail on a Managed Online Endpoint?

You are preparing an Azure Machine Learning training job that must read a large Parquet dataset stored in an Azure Data Lake Storage Gen2 account. The job runs on an Azure ML compute cluster, and you want the data to be streamed directly from the storage account without first downloading the full dataset to the compute node's local disk. You also need to avoid storing account keys in the job script. Which Azure ML datastore configuration should you use?

You are designing a reusable Azure ML pipeline that must run a data validation step, a training step, and a registration step. The pipeline will be executed repeatedly with different input datasets, and you need each step to run in its own isolated environment with pinned dependencies. You also need the pipeline to be versioned and shareable across workspaces. What should you use to define the pipeline?

You are configuring a real-time managed online endpoint in Azure Machine Learning. The model must be updated frequently with minimal downtime, and you need the ability to roll back quickly if a new version performs poorly. Which deployment strategy should you use?

You are setting up an Azure Machine Learning workspace that must support reproducible training jobs. You need to ensure that the same code, dependencies, and data are used every time a job is rerun. Which two actions should you take? (Choose two.)