Q01 - Question
You create an Azure Machine Learning workspace that uses a private endpoint. The workspace storage account and container registry are behind private endpoints. Docker image builds for environments must work. You use the Azure CLI with a YAML file to create the workspace. What should you configure?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Create a CPU compute cluster and set the image_build_compute property in the workspace YAML to its name.
- B. Rely on Azure Container Registry tasks compute to build the images.
- C. Set public_network_access to Enabled so that the default image build works.
- D. Remove the private endpoint from the workspace after you create it.
A is correct.
Explanation: When a workspace uses a private link, it can't use Azure Container Registry tasks compute for image building. By default, it uses a serverless compute cluster, but that works only when the dependent resources have no network restrictions. Because the storage account and container registry are behind private endpoints, set image_build_compute to a compute cluster name. Create that compute before you build images.
B is incorrect: A workspace that uses a private link can't use Azure Container Registry tasks compute for image building.
C is incorrect: The public_network_access property controls whether the workspace is reachable over the internet. It does not change how images are built when the dependent resources are under network restrictions.
D is incorrect: Removing the private endpoint removes the workspace from the virtual network and doesn't make it publicly accessible. It also defeats the purpose of the private endpoint.
Q02 - Question
IoT devices send temperature measurements as JSON objects at regular intervals. A team wants to train a forecasting model on one table that contains the average temperature per machine per minute. The team wants the data preparation steps to run on a schedule. Which design meets the requirement?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Send the raw JSON objects directly to a batch endpoint for scoring.
- B. Select GPU compute in Azure Machine Learning so that the JSON objects are aggregated during training.
- C. Use automated machine learning to convert the JSON objects to a table before any data is extracted.
- D. Create a data ingestion pipeline that extracts the JSON objects, converts them to a table, aggregates the data per machine per minute, and stores the prepared data in a serving layer such as Azure Blob Storage.
D is correct.
Explanation: The evidence supports D because a data ingestion pipeline is a sequence of tasks that move and transform data, and you can trigger it manually or schedule it. In this scenario, the pipeline extracts the JSON objects, converts them to a table, aggregates the data, and stores it in a serving layer.
A is incorrect: A batch endpoint is used to score new data with a trained model. It does not prepare training data.
B is incorrect: Compute selection affects how much processing power is available for training. It does not convert JSON objects to a table or create per-minute aggregates.
C is incorrect: Automated machine learning runs training jobs with different algorithms and parameters. The data must first be extracted from the source and made available in the required format.
Q03 - Question
Your organization has Azure Machine Learning workspaces in East US today. It plans to add workspaces in West US soon. Teams must register models, environments, and components once and use them from all workspaces. You use the Azure CLI to create the registry. Which design should you use?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Register the online endpoint in the registry so that its scoring URI works from both workspaces.
- B. Create the registry with only East US, and change the primary region to West US when the new workspaces exist.
- C. Create a separate registry in each workspace and copy assets between them when needed.
- D. Create one registry with eastus as the primary location and include eastus and westus in the replication locations.
D is correct.
Explanation: A registry lets you create and use assets such as models, environments, and components across workspaces, including workspaces in different regions. You define a primary region and a set of other regions when you create the registry. List the regions where you have workspaces today and plan to add in the near future. The primary location appears in the location item and in the replication_locations list.
A is incorrect: Endpoints are resources, not assets. Resources are workspace specific, and an online endpoint has a scoring URI that is unique to one instance in one workspace.
B is incorrect: You can't change the primary region after you create the registry. You can update only the other regions.
C is incorrect: Registries exist to store assets in a central repository and share them across workspaces. A registry is not created inside a workspace, and copying assets between registries adds work that a single registry avoids.
Q04 - Question
A web shop wants a model to recommend other catalog items as soon as a customer selects a product. The recommendations must appear when the product details page loads. Select two statements that apply to the deployment design.
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Multiple choice
- A. Deploy the model to a batch endpoint and run scoring once a week.
- B. Deploy the model to a real-time endpoint.
- C. Use a compute cluster that the workspace scales down to 0 nodes when no data is waiting to be processed.
- D. Plan for compute that is always on, because the model must be available for immediate predictions.
B and D are correct.
Explanation: The evidence supports B and D because a model that must score new data as it comes in, such as for a website, needs real-time predictions. A real-time endpoint uses compute that is always available and can return results almost immediately. You can't pause or stop that compute, so it is always on.
A is incorrect: Batch scoring suits predictions that are consumed at certain times, such as a weekly forecast. It does not return recommendations when a customer selects a product.
C is incorrect: Scaling a compute cluster down to 0 nodes applies to batch scoring, where compute is provisioned when scoring is triggered. A real-time endpoint must stay available.
Q05 - Question
A data science team has historical weekly sales records. The team must build a model that predicts the number of units that will be sold in each of the next several weeks. Before the team selects algorithms and evaluation metrics, it must identify the machine learning task. Which task should the team choose?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Classification
- B. Time-series forecasting
- C. Computer vision
- D. Natural language processing (NLP)
B is correct.
Explanation: The evidence supports B because the model must predict future numerical values from time-series data, which describes time-series forecasting. The task determines which algorithms you can use to train the model, and the available performance metrics also depend on the task.
A is incorrect: Classification predicts a categorical value, such as whether a patient has diabetes. A unit count is a numerical value.
C is incorrect: Computer vision classifies images or detects objects in images. The data in this scenario is sales records, not images.
D is incorrect: Natural language processing (NLP) extracts insights from text. The data in this scenario is numerical sales history, not text.
Q06 - Question
You are writing a Bicep template for a Microsoft.MachineLearningServices/workspaces resource that uses API version 2025-12-01. You must plan for properties that are hard to change after deployment. Select two statements that are correct.
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Multiple choice
- A. You can change the keyVault property after the workspace is created to move to a different key vault.
- B. You can't change the storageAccount property after the workspace is created.
- C. After you switch managedNetworkKind from V1 to V2, you can switch back to V1 if needed.
- D. You can switch managedNetworkKind from V1 to V2 to get granular access controls.
B and D are correct.
Explanation: The storageAccount property holds the ARM ID of the associated storage account and can't be changed once the workspace is created, so choose the account before you deploy. The managedNetworkKind property lets you switch from V1 to V2 for granular access controls.
A is incorrect: The keyVault property holds the ARM ID of the key vault associated with the workspace. It can't be changed once the workspace is created.
C is incorrect: After V2 is enabled for managedNetworkKind, you can't switch back to V1. Plan the change before you make it.
Q07 - Question
A group of data scientists must register new models and delete outdated models in an Azure Machine Learning registry. They must also use the assets in workspaces. They must not be able to create, update, or delete registries. Which role assignment meets the requirement?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. The built-in Reader role.
- B. The built-in Contributor role.
- C. A custom role that has registries/read, registries/assets/read, registries/assets/write, and registries/assets/delete permissions.
- D. The built-in Owner role.
C is correct.
Explanation: To let users read, create, and delete assets, grant the read permissions plus the assets/write and assets/delete permissions. A custom role that includes only these permissions does not include registries/write or registries/delete, so the users can't create, update, or delete registries.
A is incorrect: The built-in Reader role lets users only read and use assets. It does not allow them to create or delete assets.
B is incorrect: The built-in Contributor role allows users to create, update, and delete registries. This does not meet the restriction.
D is incorrect: The built-in Owner role also allows users to create, update, and delete registries. This does not meet the restriction.
Q08 - Question
Your team trains a model that needs a specific set of Python packages and environment variables. You must run the same training job on a compute instance and on a compute cluster. The package set must be versioned in the Azure Machine Learning workspace, and the setup must be reproducible and auditable. What should you create?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. A datastore that stores the list of required packages.
- B. A custom environment that uses a base Docker image and a Conda YAML file.
- C. A curated environment that you use as is, with the extra packages installed at run time.
- D. A component for each required package.
B is correct.
Explanation: An environment specifies the software packages, environment variables, and software settings for training and scoring scripts. Environments are managed and versioned entities in the workspace, and they support reproducible, auditable, and portable workflows across computes. A custom environment can use a base Docker image with a Conda YAML file for further customization.
A is incorrect: A datastore keeps the connection information for a storage service. It does not define packages or software settings for a job.
C is incorrect: Curated environments are intended to be used as is. Use a custom environment when you must control the packages and dependencies.
D is incorrect: A component is a self-contained piece of code that completes one step in a pipeline. It does not define the software environment for a job.
Q09 - Question
A team will train a model in Azure Machine Learning on a smaller tabular dataset. The team wants a cost-effective compute choice. It also wants to learn from each training run whether to scale compute up or down. Which approach should the team use?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Select GPU compute for all training jobs, because GPUs are required for tabular data.
- B. Distribute training by using Spark compute, because it works without changes to the training scripts.
- C. Start with CPU compute, and monitor training time and compute utilization for each run.
- D. Select memory optimized compute, because it is the only option for datasets with fewer rows.
C is correct.
Explanation: The evidence supports C because for smaller tabular datasets, a CPU is sufficient and cost-effective. Each time you train a model, you should monitor how long it takes and how much compute your code uses to know whether to scale compute up or down.
A is incorrect: GPUs are more powerful and efficient for unstructured data such as images or text. For tabular data, use a GPU only if CPU compute is insufficient.
B is incorrect: Spark compute can distribute model training, but it requires you to rewrite your training scripts. It is also not needed for a smaller dataset.
D is incorrect: Memory optimized compute has a high memory-to-CPU ratio and suits larger datasets or notebook work. General purpose compute has a balanced ratio and suits testing and development with smaller datasets.
Q10 - Question
An organization needs one cloud service to train, register, deploy, and manage its own machine learning models. Data scientists want a UI-based experience, and engineers want a code-first experience that uses the Python SDK or CLI. The service must also provide on-demand compute for training jobs and built-in support for evaluating responsible AI metrics. Which service should the organization choose?
Domain: Design and implement an MLOps infrastructure (15–20%) Type: Single choice
- A. Azure Machine Learning
- B. Foundry Tools
- C. Microsoft Fabric
- D. Azure Databricks
A is correct.
Explanation: The evidence supports A because Azure Machine Learning is a cloud service for training, deploying, and managing machine learning models. You can use the studio for a UI-based experience, or the Python SDK or CLI for a code-first experience. It also provides on-demand compute and built-in support for visualizing and evaluating responsible AI metrics.
B is incorrect: Foundry Tools is a collection of prebuilt models offered as APIs. Some models can be customized, but the requirement is to train and manage the organization's own models.
C is incorrect: Microsoft Fabric is an integrated analytics platform for workflows between data analysts, data engineers, and data scientists, with results visualized in Power BI reports. It is not the service described for the registration, deployment, and responsible AI evaluation requirements.
D is incorrect: Azure Databricks is a data analytics platform that uses distributed Spark compute. You can train models in it, but the requirements point to Azure Machine Learning.