Operationalizing Machine Learning and Generative AI Solutions v1.0

A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution. Choose three.
NOTE: Each correct selection is worth one point.

DRAG DROP -
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.

HOTSPOT -
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.

A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?

HOTSPOT -
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.

DRAG DROP -
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
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.

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?

A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?

HOTSPOT -
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:

You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

HOTSPOT
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Case Study
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This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.

To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.

A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.


To start the case study
-

To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the "Question" button to return to the question.


Background
-

Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States. Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions.

Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues.

Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk.


Current Environment
-

Fabrikam Inc. operates a single Azure subscription that has the following components:

• Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
• Azure AI Search indexing curated analytical documents and reference materials
• A small set of Python-based training scripts maintained by data scientists
• Azure OpenAI Service with deployed foundational models
• A Microsoft Foundry resource for building a RAG-based solution

Evaluation data has manually defined expected responses.

The current challenges faced by the data science team include the following:

• Model training jobs are run manually from notebooks.
• Experiment tracking is inconsistent
• Model versions are registered without standardized metadata.
• Deployment is performed manually by data scientists, with limited rollback capability.
• The team has no standardized evaluation process for generative AI outputs.

The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage.


Business Requirements
-

Fabrikam Inc. has the following business requirements for the modernization initiative:

• Provide a conversational interface that answers analytics questions by using internal documents and datasets.
• Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
• Enable repeatable and auditable model training and deployment processes.
• Support experimentation to compare prompt strategies and fine-tuned models.
• Align the model with the ranked preferences and optimize behavior for the long term.
• Minimize disruption to existing analytics workloads during rollout.


Technical Requirements
-

To support the business goals, Fabrikam Inc. identifies these technical requirements:

• Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
• Implement experiment tracking and model versioning for all training jobs.
• Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
• Deploy traditional machine learning models with support for staged rollout and rollback.
• Improve RAG-based solution output quality.
• Use the existing evaluation datasets that are based on real data with input-output pairs.
• Apply advanced fine-tuning techniques only when prompt engineering is insufficient


Issues and Constraints
-

Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure.

Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads.


Problem Statement
-

Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities.


You need to deploy the RAG-based chat application that meets Fabrikam Inc.’s business and technical requirements.

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

NOTE: Each correct selection is worth one point.

Case Study -

This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.

To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.

A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.


To start the case study -

To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the "Question" button to return to the question.


Background -

Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States. Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions.

Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues.

Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk.


Current Environment -

Fabrikam Inc. operates a single Azure subscription that has the following components:

• Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
• Azure AI Search indexing curated analytical documents and reference materials
• A small set of Python-based training scripts maintained by data scientists
• Azure OpenAI Service with deployed foundational models
• A Microsoft Foundry resource for building a RAG-based solution

Evaluation data has manually defined expected responses.

The current challenges faced by the data science team include the following:

• Model training jobs are run manually from notebooks.
• Experiment tracking is inconsistent
• Model versions are registered without standardized metadata.
• Deployment is performed manually by data scientists, with limited rollback capability.
• The team has no standardized evaluation process for generative AI outputs.

The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage.


Business Requirements -

Fabrikam Inc. has the following business requirements for the modernization initiative:

• Provide a conversational interface that answers analytics questions by using internal documents and datasets.
• Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
• Enable repeatable and auditable model training and deployment processes.
• Support experimentation to compare prompt strategies and fine-tuned models.
• Align the model with the ranked preferences and optimize behavior for the long term.
• Minimize disruption to existing analytics workloads during rollout.


Technical Requirements -

To support the business goals, Fabrikam Inc. identifies these technical requirements:

• Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
• Implement experiment tracking and model versioning for all training jobs.
• Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
• Deploy traditional machine learning models with support for staged rollout and rollback.
• Improve RAG-based solution output quality.
• Use the existing evaluation datasets that are based on real data with input-output pairs.
• Apply advanced fine-tuning techniques only when prompt engineering is insufficient


Issues and Constraints -

Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure.

Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads.


Problem Statement -

Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities.


You need to recommend a solution to address Fabrikam Inc.’s limited rollback capability.

Which deployment approach should you recommend?

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 in the review screen.

You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.

You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

You need to perform the task that must be completed before you can add the new kernel.

Solution: Delete the Python 3.6 - AzureML kernel.

Does the solution meet the goal?