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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production |
| Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads |
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NEW QUESTION # 52
A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
[A]
Engineer Permissions (Hub Scope)
Engineers require the ability to manage infrastructure, networking, and global security settings.
Role: Azure AI Administrator or Contributor.
Assignment Scope: Assign at the Foundry Hub/Resource level.
Capabilities: They can manage virtual networks, customer-managed keys, and shared connections (e.g., Azure OpenAI) that all projects inherit.
[C]
Developer Permissions (Project Scope)
Developers should be restricted from changing the underlying resource configurations but need full access to their specific AI workloads.
Role: Azure AI Developer or Project Contributor.
Assignment Scope: Assign strictly at the Foundry Project level.
Capabilities: This allows them to create and update project assets (agents, flows, evaluations) and deploy models without permission to modify Hub-level infrastructure or security settings Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/rbac-foundry
NEW QUESTION # 53
Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
NEW QUESTION # 54
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?
Answer: C
Explanation:
For Azure Machine Learning SDK v2, Microsoft recommends MLflow Tracking for experiment logging.
When a requirement specifically calls for logging a list of numerical metric values , the appropriate operation is log_batch() . Microsoft provides an explicit SDK v2 migration example in which a list of numeric values is converted into MLflow Metric objects and submitted through MlflowClient().log_batch().
Technically, the concrete Python implementation uses the tracking client:
MlflowClient().log_batch(run_id, metrics=metrics)
where metrics contains a sequence of Metric objects representing the numerical values. Microsoft likewise documents batch metric logging as preferable when multiple metrics or multiple values of the same metric need to be recorded efficiently.
mlflow.log_metric() records an individual numeric metric value. It can be invoked repeatedly, but Microsoft specifically demonstrates log_batch() for a curve or list of numeric values . mlflow.log_artifact() stores files and other artifacts rather than numerical tracking metrics, while mlflow.log_image() is intended for images and therefore does not satisfy the requirement.
Therefore, A is the intended answer. In actual Python SDK v2 code, this operation is normally invoked as MlflowClient().log_batch() .
Study Guide Reference: Implement machine learning model lifecycle and operations - Azure Machine Learning experiments, MLflow tracking, metric logging, experiment observability, and SDK v2 migration.
NEW QUESTION # 55
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
Answer: B
Explanation:
GPT-4o is OpenAI ' s multimodal model that accepts both text and image inputs natively. The ' o ' in GPT-4o stands for ' omni, ' reflecting its ability to process multiple modalities in a single inference call. It is available in Microsoft Foundry for both inference and fine-tuning, and it supports vision inputs alongside text prompts, making it the correct choice for any fine-tuning scenario requiring multimodal input. Davinci-002 (option A) is a legacy text-completion model with no vision capabilities and limited fine-tuning support for modern chat formats. GPT-3.5-Turbo (option C) is a text-only chat model that does not accept image inputs. GPT-4 (option D) has a standard version that is text-only; the vision variant GPT-4V is distinct from GPT-4o and has more limited fine-tuning availability in Foundry. For multimodal text plus image fine-tuning, GPT-4o is the only correct choice.
Microsoft Learn Reference Topic: Azure OpenAI Service models available for fine-tuning - GPT-4o multimodal capabilities
NEW QUESTION # 56
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 a solution to address Fabrikam Inc.'s limited rollback capability. Which deployment approach should you recommend?
Answer: C
Explanation:
Scenario:
Current Environment: Deployment is performed manually by data scientists, with limited rollback capability.
The correct deployment type to address this problem is Managed online endpoints with traffic splitting.
Native traffic control: Managed online endpoints naturally support native blue-green deployments under a single HTTP endpoint.
Granular traffic splitting: You can deploy a new model version (e.g., green) with 0% live traffic, safely test it, and then incrementally shift traffic from the old version (e.g., blue).Instant rollbacks:
If the new model shows bugs or degrades performance, you can immediately change the traffic percentage configuration back to 100% for the old deployment. This eliminates the risks of limited rollback capabilities.
No infrastructure overhead: Unlike setting up manual routing, Azure handles the underlying infrastructure and routing mechanisms in a turnkey fashion.
Incorrect:
[Not A]
VM-hosted REST APIs: This forces you to build, maintain, and configure your own custom load balancers and deployment scripts to handle routing and rollbacks, which increases operational risk and complexity.
[Not B]
Azure Kubernetes Service with blue-green switching: While it supports blue-green deployments, it requires you to manage complex Kubernetes infrastructure, service meshes, or ingress controllers manually to handle the traffic switching.
[Not D]
Batch endpoints: These are designed for long-running, asynchronous processing of large data batches rather than real-time requests where instant live-traffic rollback is required.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints
NEW QUESTION # 57
......
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