Microsoft Operationalizing Machine Learning and Generative AI Solutions (AI-300)

You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.windows.net/data1.

You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.

You need to select the parameters of the AzureBlobDatastore class that will point to the target datastore and authorize access to it.

Which parameters should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have an Azure Machine Learning workspace.

You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace. The component definition is stored in the local file ./components/train/train.yml.

You write code to connect to the workspace by using the ml_client object and import all required libraries.

You need to complete the remaining code.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1.

You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone.

Which four actions should you perform in sequence?

You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records.

You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:

• number of non-empty values

• average value

• standard deviation

• minimum and maximum values

• 25th, 50th, and 75th percentiles

You need to complete the Python code that will generate the summary statistics.

Which code segments should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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.

An organization provisions Azure Machine Learning workspaces for development, test, and production environments.

Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.

You need to deploy Azure Machine Learning resources in a consistent and controlled manner.

Solution: Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.

Does the solution meet the goal?

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.

An organization provisions Azure Machine Learning workspaces for development, test, and production environments.

Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.

You need to deploy Azure Machine Learning resources in a consistent and controlled manner.

Solution: Create Azure Machine Learning workspaces manually in the Azure portal for each environment.

Does the solution meet the goal?

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.

An organization provisions Azure Machine Learning workspaces for development, test, and production environments.

Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.

You need to deploy Azure Machine Learning resources in a consistent and controlled manner.

Solution: Clone an existing Azure Machine Learning workspace to create additional environments.

Does the solution meet the goal?

You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2.

You configure the regression job by using the following script:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.

You must deploy the model to an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.

You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.

What should the entry script do?

You build and manage a model by using Azure Machine Learning workspace.

Before you deploy the model, you must create a Responsible AI dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:

• metrics that show real-world impact on an outcome of interest due to taking a treatment policy

• examples with minimal changes to a particular data point such that the model's prediction changes

You need to implement the components for the Responsible AI dashboard.

Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component 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.

You have several machine learning models registered in an Azure Machine Learning workspace.

You must use the Fairlearn dashboard to assess fairness in a selected model.

Which three 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.

You create a multi-class image classification model with automated machine learning in Azure Machine Learning.

You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset.

Which data format should you use?

A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.

The tuning process must run multiple training trials without manually modifying the training script for each run.

You need to automate hyperparameter tuning for the training job.

What should you do?

You create an Azure Machine Learning workspace.

You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

You need to complete the Python code to log the table.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy for an Azure ML Python SDK v2-based model training.

The policy uses a slack_factor set to 0.1, an evaluation interval set to 1, and an evaluation delay set to 5.

You need to evaluate the outcome of the early termination policy.

What should you evaluate? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You create an Azure Machine Learning workspace.

You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.

You need to complete the Python SDK v2 code to include a training script, environment, and compute information.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You use Azure Machine Learning to train a model.

You must use Bayesian sampling to tune hyperparameters.

You need to select a learning_rate parameter distribution.

Which two distributions can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You manage a Microsoft Foundry project. You build a solution that uses a set of PDF documents.

You require two large language models (LLMs):

• An embedding model must help categorize the documents.

• A general-purpose model must generate semantically and contextually accurate output based on the documents.

You need to select benchmarks to observe the quality of the models.

Which metrics should you use for the benchmarks? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You need to standardize how Fabrikam Inc. manages machine learning assets.
Which action should you perform first?

You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

You are reviewing the results of experiment1 by using the following code segment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace by using the Python SDK v2.

You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an autoscaling compute target and a data asset for the job.

You need to configure the resources for the job.

Which resource configuration should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have a deployment of an Azure OpenAI Service base model.

You plan to fine-tune the model.

You need to prepare a file that contains training data.

Which file format should you use?

You manage an Azure Machine Learning workspace by using the Python SDK v2.

You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.

The cluster definition includes the following properties and values:

• name="mlcluster1"

• size="STANDARD_DS3_v2"

• min_instances=1

• max_instances=4

• tier="dedicated"

The cost of the compute resources must be minimized when a workload is active or idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.

You need to modify the cluster properties to minimize the cost of compute resources.

Which properties should you modify? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.

Provisioning must be triggered by changes in the templates and executed without manual intervention.

You need to automate resource provisioning.

Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

You manage an Azure OpenAI deployment of the gpt-4o base model.

You plan to fine-tune the deployed model.

You need to prepare a file that contains training data.

Which keys should you include in each line of the training data file? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project and a CSV file stored in Azure Blob Storage.

You plan to add the CSV file as the grounding data to the project for RAG by using the file data type.

You need to specify the URL schema to designate the blob location.

Which URL schema should you specify?

You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.’s issues, constraints, and technical requirements.

What should you implement?

You manage a Microsoft Foundry project.

You plan to create a search index by using the Microsoft Foundry SDK.

You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.

How should you complete the item code segment? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

An organization operates a customer-facing generative AI chat service deployed by using Microsoft Foundry. The service processes a predictable, sustained volume of requests. The service must meet strict response time service-level agreements (SLAs) during peak business hours.

The organization requires that:

• Model responses remain consistent during sustained high traffic.

• Latency does not degrade during peak usage periods.

• Capacity planning avoids throttling and unpredictable performance.

You need to ensure that the deployed foundation model can reliably handle sustained, high-volume traffic while meeting performance and availability requirements.

What should you do?

You manage an Azure Machine learning workspace. You develop a machine learning model.

You must deploy the model to use a low-priority VM with a pricing discount.

You need to deploy the model.

Which compute target should you use?

A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.

The team needs to introduce a new version of a model to production without disrupting existing users.

The team must validate the new version before full rollout.

You need to reduce risk during deployment.

What should you do?

A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production.
When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems.
You need to configure the monitoring solution to meet the requirements.
Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.