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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.

A) A model that offers enterprise governance controls when workloads process regulated business data
B) The largest available model to simplify operational management
C) A model that is optimized for conversational reasoning when deploying an interactive assistant
D) The smallest available model to minimize the usage cost
E) A model that supports multiple input types when workloads require combined text and image analysis

2. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) Manual approval gate
B) Azure Monitor alerts
C) GitHub pull request checks
D) Conditional logic in pipeline

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

A) Register assets in the Azure Machine Learning registry.
B) Deploy a managed online endpoint.
C) Create a new Microsoft Foundry project.
D) Create a shared Azure Machine Learning workspace.

4. Hotspot Question
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.

5. Drag and Drop Question
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load.
You need to enable performance observability.
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.

Solutions: