A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.

The team must:

Track prompt changes with a clear history for audit and rollback.

Compare prompt variants in parallel without affecting the prompt used in the production environment.

You need to select the appropriate source control approach for each requirement.

What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control 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 a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.

Recent analysis shows that:

Retrieved results frequently include duplicated content from the same document.

Retrieved chunks sometimes span unrelated policy sections.

You review the following retrieval and ingestion configurations:

You need to reduce duplicated retrieval results and improve chunk relevance across policy sections.

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 create a multi-class image classification deep learning model.

The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images and retrain the model.

You need to use the Azure Machine Learning Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month ' and interval set to " 1:

Which three classes should you instantiate 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 manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.

You plan to use the folder data asset for data wrangling during interactive development.

You need to access and load the folder data asset into a Pandas data frame.

Which method should you use to achieve this goal?

You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?

You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint. You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.

Solution: Add a compute resource to the workspace.

Does the solution meet the goal?

An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.

An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.

You need to change the state of the model version to meet the requirements.

What should you do?

You are preparing training data for a fine-tuning job in Microsoft Foundry.

Real production conversations cannot be used due to compliance requirements.

You need to generate synthetic interaction data that can be used for fine-tuning a generative model.

What should you do?

You use Azure Machine Learning to tram a model.

You must use Baylean sampling to Tune hyperparaters.

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 have an existing GitHub repository containing Azure Machine Learning project files.

You need to clone the repository to your Azure Machine Learning shared workspace file system.

Which four 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.

NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

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

You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.

You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.

You need to configure store1.

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

NOTE: Each correct selection is worth one point.

You train and register an Azure Machine Learning model

You plan to deploy the model to an online endpoint

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution:

Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.

Does the solution meet the goal?

You have an Azure Machine Learning workspace. You are running an experiment on your local computer.

You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.

In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

You have an Azure subscription named Sub1 that contains an Azure

• a registered MLflow model named Model1

• an online endpoint named Endpoint1

Outbound network connectivity from Endpointl is blocked. You need to deploy ModeM to Endpointl. What should you do first?

You create an Azure Machine Learning workspace.

You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.

You need to implement the method to log the string metrics.

Which method should you use?

You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.

You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.

You need to continue to log metrics and artifacts from your data science code.

What should you do?

A team runs training jobs by using multiple Azure Machine Learning pipelines.

The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.

You need to configure the workspace so that runtime dependencies are consistent and reusable.

Which four 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.

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 work in Microsoft Foundry with a prompt flow.

You must manually evaluate prompts and compare results across prompt variants.

You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.

Solution: Create prompt variants and compare their outputs in the Evaluation experience.

Does the solution meet the goal?

You need to recommend an experiment-tracking strategy that ensures consistent experiment results.

What should you recommend?

Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .

You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.

Which deployment approach should you recommend?

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

What should you implement?

Fabrikam Inc. needs to improve the performance of a GPT-5 model based on the stated technical requirements.

Which action should you perform first?

You need to configure an optimization method to meet Fabrikam Inc.’s technical requirements.

Which strategy should you apply first? 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 run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.

There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.

You need to exclude the 20 files. What should you do?

You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:

• Provide near real-time inferencing.

• Enable endpoint and deployment level cost estimates.

• Support logging to Azure Log Analytics.

You need to configure the deployment solution.

What should you configure? 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.

You work in Microsoft Foundry with a prompt flow.

You must manually evaluate prompts and compare results across prompt variants.

You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.

Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.

Does the solution meet the goal?

A team develops and manages a conversational assistant by using Microsoft Foundry.

The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.

You need to automate evaluations as part of CI.

What should you configure?

You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target an environment and a training script You need to prepare information to submit a training job. Which class should you use?

A team provisions an Azure Machine Learning environment by triggering pull requests.

Deployments must be automated, auditable, and require approval before running.

You need to select a deployment automation tool.

Which tool should you use?

You manage an Azure Machine Learning workspace.

You schedule a pipeline job by using Azure Machine Learning Python SDK v2.

You need to decide whether the time-based schedule with cron expression is implemented correctly.

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 create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.

What should you do first?

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?