Case Study 1 - Fabrikam Inc.
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 an experiment-tracking strategy that ensures consistent experiment results. What should you recommend?

• A. Application Insights logs
• B. Azure Monitor alerts
• C. Azure Machine Learning job output logs
• D. MLflow experiment tracking

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A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?

• A. Store prompts as versioned files in a Git repository.
• B. Embed prompts directly in application configuration files.
• C. Register prompts as datasets in the Azure Machine Learning workspace.
• D. Persist prompts in Azure Blob Storage with folder-level organization.

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Hotspot Question
You have an Azure Machine Learning workspace named Workspace1.
You plan to train an image classification model by using Automated ML in Workspace1.
You need to complete the provided Azure Machine Learning Python SDK v2 code to bring labeled image data as input for model training.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: azure.ai.ml.constants
azure.ai.ml.constants is the official SDK v2 submodule where the AssetType enum resides.
Box 2: MLTABLE
AssetType.MLTABLE: Automated ML for Computer Vision tasks (such as image classification and object detection) specifically requires your input data and corresponding label annotations to be provided via an MLTable asset type. This structure points to a folder containing your dataset configurations and your .jsonl bounding box or classification files.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models

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: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?

• A. Yes
• B. No

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Hotspot Question
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.

Explanation:
Box 1: 91%
Box 2: Every interval when metrics are reported, starting at evaluation interval 5 Every interval when metrics are reported, starting at evaluation interval 5 is the best description for the run termination schedule in this scenario.
evaluation_interval = 1: The policy checks for potential termination every time the training script logs the primary metric.
evaluation_delay = 5: Policy evaluation is suspended for the first 5 intervals to prevent the premature termination of training runs before they have time to stabilize.
Combined Behavior: Once the evaluation_delay threshold of 5 is met, the policy applies at every subsequent multiple of the evaluation_interval (Intervals 5, 6, 7, etc.).
Reference:
https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html