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Microsoft AI-300 Exam Actual Questions
Operationalizing Machine Learning and Generative AI Solutions (Page 7 )

Updated On: 18-Jul-2026
Page 3 of 15
PDF with 142 Questions

A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

  1. Generate synthetic interaction data.
  2. Configure content filters.
  3. Apply a blocklist.
  4. Enable observability metrics.

Answer(s): A

Explanation:

In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
1. Generate Synthetic Interaction Data Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
2. Compare Prompt Variants Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
3. Controlled Evaluation To measure performance objectively, run automated Eval Runs.


Reference:

https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow



DRAG DROP (Drag and Drop is not supported) (Drag and Drop is not supported)
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.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Configure a GitHub actions workflow. Trigger provisioning on repository changes.
To provision Azure Machine Learning (AML) resources using Infrastructure as Code (IaC) with automated GitHub Actions, you must define your infrastructure in a Bicep or ARM template and configure a workflow to trigger on repository changes.
Infrastructure as Code (Bicep Template) Bicep is the recommended declarative language for Azure resources. A standard AML workspace requires dependent resources like Storage Accounts, Key Vaults, and Application Insights.
GitHub Actions Workflow The workflow automates deployment by listening for push or pull request events to specific branches.
Key Setup Requirements *-> [Box 2] Service Principal: Create an Azure Service Principal with Contributor access to the target subscription.
* GitHub Secrets: Store the Service Principal JSON as AZURE_CREDENTIALS in your repository settings.
* RBAC: Ensure the Service Principal has permissions to create Key Vaults and Storage Accounts.
Box 2: Use an Azure Service principal. Authenticate securing during workflow execution.


Reference:

https://medium.com/@vmehmeri/infrastructure-as-code-with-azure-devops-and-arm-templates-2145468e394e



An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

  1. Enable workspace-level Git integration and sync assets between repositories.
  2. Publish the asset as a pipeline component.
  3. Create a shared Azure Machine Learning environment that includes the asset.
  4. Publish the asset to an Azure Machine Learning registry.

Answer(s): D

Explanation:

To share validated assets between development and production workspaces while maintaining centralized governance and version control, you should use Azure Machine Learning Registries. Microsoft Learn
Unlike standard workspaces, registries are workspace-agnostic. They act as a central catalog that decouples assets from individual environments, allowing you to "promote" a model or environment from Dev to Prod without manual duplication or data drift.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops



An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?

  1. Azure Firewall rules
  2. Private endpoints
  3. Network security groups
  4. Service endpoints

Answer(s): B

Explanation:

To ensure an Azure Machine Learning (AML) workspace handling sensitive data is not accessible from the public internet, you must disable the Public Network Access flag and implement Private Endpoints. This configuration creates a private link between your Azure Virtual Network (VNet) and the workspace, ensuring traffic never traverses the public internet.


Reference:

https://www.azadvertizer.net/azpolicyadvertizer/438c38d2-3772-465a-a9cc-7a6666a275ce.html



A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?

  1. Model
  2. Component
  3. Environment
  4. Pipeline

Answer(s): A

Explanation:

In an Azure Machine Learning classification workflow, you should register Models.
Registration creates a versioned asset in your workspace or a centralized registry, which is essential for ensuring that artifacts are reusable, governed, and trackable across different projects and environments.
Key Assets for Reuse and Governance To maintain a consistent and governed workflow, you should focus on registering these specific assets: Models: The primary artifact. Registering a model allows you to track its lineage (which experiment created it), version it, and deploy it consistently across environments.
Components: These are self-contained pieces of code that perform specific steps in a pipeline (e.g., data cleaning, training). Registering them allows different teams to reuse the same "traditional" classification logic without rewriting code.
Environments: Encapsulates the software dependencies (Python packages, Docker images) required for your model to run. Registering these ensures reproducibility across different compute targets.
Data Assets: Registering your training and testing datasets as versioned assets ensures that you can always audit exactly what data was used to train a specific model version.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-v2



HOTSPOT (Drag and Drop is not supported)
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: load To load your data into a Pandas DataFrame, use the following structure: import mltable
# Line 2: Point specifically to the folder containing the 'MLTable' file tbl = mltable.load("./sample_data/MLTable")
# Line 3: Materialize the data df = tbl.to_pandas_dataframe()
Box 2: ./sample_data/MLTable
To materialize data using mltable, the path must point to the directory containing the MLTable file (a YAML metadata file) or directly to the file itself.


Reference:

https://github.com/paulshealy/azuremldocs/blob/master/how-to-mltable.md



HOTSPOT (Drag and Drop is not supported)
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.
You need to complete the Python SDK v2 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.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: abfss Supported paths When you provide a data input/output to a Job, you must specify a path parameter that points to the data location. This table shows both the different data locations that Azure Machine Learning supports, and examples for the path parameter:
* A path on Azure Storage https://<account_name>.blob.core.windows.net/<container_name>/<path> abfss://<file_system>@<account_name>.dfs.core.windows.net/<path>
* A path to a Data Asset azureml:<my_data>:<version>
* Etc.
Note: # Supported paths include: # local: './<path>' # blob: 'https://<account_name>.blob.core.windows.net/<container_name>/<path>' # ADLS gen2: 'abfss://<file_system>@<account_name>.dfs.core.windows.net/<path>/' # Datastore: 'azureml://datastores/<data_store_name>/paths/<path>'
Box 2: uri_folder You can create three data asset types:
* Folder, Reference a single folder V2 API: uri_folder V1 API: FileDataset
You must read/write a folder of parquet/CSV files into Pandas/Spark.
Deep-learning with images, text, audio, video files located in a folder.
Incorrect: Not mltable: * Table, Reference a data table V2 API: mltable V1 API: TabularDataset
You have a complex schema subject to frequent changes, or you need a subset of large tabular data.
AutoML with Tables.
Not uri_file: * File, Reference a single file V2 API: uri_file V1 API: FileDataset
Read/write a single file - the file can have any format.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets



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.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python train.py --training_data training_data
Does the solution meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

Correct: * python script.py --training_data ${{inputs.training_data}} The scipt is named script.py. For the parameter use ${{inputs.training_data}}
Incorrect:
* python script.py --training_data dataset1.csv * python script.py dataset1.csv * python train.py --training_data training_data
Note: Read a TabularDataset, Example In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:
* Details omitted* job = command( code="./src", # Local path where the code is stored *-> command="python train.py --inputs ${{inputs.input_data}}", inputs=my_job_inputs, environment="<environment_name>:<version>", compute="cpu-cluster", )


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2



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