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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 3: Design and implement generative AI solutions | - Large language model integration
|
| Topic 4: Operationalizing machine learning solutions | - Deployment and monitoring
|
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NEW QUESTION # 166
You create a multi-class image classification model with automated machine learning in Azure Machine Learning.
You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset.
Which data format should you use?
Answer: D
Explanation:
Azure Machine Learning, you should use the JSON Lines (.jsonl) format.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images?view=azureml-api-2
NEW QUESTION # 167
Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
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.
Answer:
Explanation:
NEW QUESTION # 168
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?
Answer: D
Explanation:
Azure Machine Learning ' s model registry supports three lifecycle states: Active (the default, fully usable state), Archived (not deployable but still accessible for review), and Deleted (permanently removed).
Archiving is precisely designed for this scenario: it ensures that operations teams cannot accidentally select the old version for a new deployment, while compliance officers can still inspect it, compare its metrics to newer versions, and reactivate it quickly if a rollback is needed. Deleting (option B) would permanently remove the version, making compliance review and rollback impossible and violating both requirements.
Unregistering (option D) is not a standard Azure ML operation; deletion is the removal action. Archiving the training dataset (option A) is unrelated to the model version ' s deployability. Archiving creates a soft-block on deployment while preserving the full artifact history.
Microsoft Learn Reference Topic: Manage model versions in the Azure Machine Learning registry - Archive and lifecycle management
NEW QUESTION # 169
Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?
Answer: A
Explanation:
A model registry provides centralized management of models, including versioning, lineage tracking, and governance. This enables teams to discover, share, and reuse models efficiently.
Simple storage solutions like blob storage lack these advanced capabilities and do not support proper lifecycle management.
NEW QUESTION # 170
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?
Answer: D
Explanation:
Scenario: To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
To centrally manage data assets, models, and environments across multiple Azure Machine Learning workspaces, you should Create a shared Azure Machine Learning workspace first.
The workspace serves as the top-level resource for your machine learning activities, providing a centralized place to view and manage the artifacts you create. While Registries are used to share assets (like models and environments) across existing workspaces, you must have a workspace as a prerequisite to create or use those assets in a project context.
Key Management Options
Azure provides several ways to organize and centralize your machine learning operations:
Shared Workspace: The primary container for managing data, compute, and experiments within a project team.
Registries: Used specifically for MLOps to decouple assets from specific workspaces, allowing them to be promoted through development, test, and production environments.
Hub Workspaces: A newer feature that groups multiple project workspaces under a single "hub" to share security settings, connections, and compute resources.
Reference:
https://docs.azure.cn/en-us/machine-learning/concept-workspace
NEW QUESTION # 171
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