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| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases |
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NEW QUESTION # 28
A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?
Answer: D
Explanation:
GitHub Actions is the best tool for this scenario.
Pull Request Integration: GitHub Actions is a native continuous integration and continuous delivery (CI/CD) platform built directly into GitHub. It can natively listen for repository events- such as triggering a workflow automatically when a pull request is opened, updated, or merged.
Required Approvals: It provides built-in governance through Environments. You can configure specific deployment environments (e.g., Production) to enforce required reviewers, ensuring a deployment cannot run until an authorized user manually approves it.
Auditable Logs: Every workflow execution, event trigger, and manual approval is tracked chronologically within GitHub. This provides a highly transparent, centralized audit trail for compliance requirements.
Reference:
https://learn.microsoft.com/en-us/azure/developer/github/github-actions
NEW QUESTION # 29
You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
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: A,B
Explanation:
Here is the correct and optimized architectural design for the solution.
1. Data Engineering & Registration
Connect Data Lake: Create an Azure ML Datastore pointing to your Azure Data Lake Storage (ADLS) Gen2.
[D] -> Modern Data Asset: Register the CSV data as an MLtable (the modern replacement for Tabular Datasets).
Schema Enforcement: Define types (e.g., CustomerID as string, Churn as boolean) in the MLTable YAML file.
Lazy Loading: MLtable loads data lazily during training to prevent out-of-memory errors on massive datasets.
2. Compute Strategy
[C] -> Compute Cluster (Scale-Out): Use an Azure ML Compute Cluster (AmlCompute) for actual large-scale model training.
Auto-Scaling: Configure the cluster to scale from 0 to N nodes so you only pay for compute during active jobs.Spot VMs: Leverage Azure Spot Virtual Machines on the cluster to reduce training costs by up to 80%.
Incorrect:
[Not E] Compute Instance (Development): Use this only as your workstation for writing code, Jupyter notebooks, and light debugging.
Reference:
https://medium.com/henkel-data-and-analytics/how-to-use-azure-ml-studio-an-eye-opening-model-training-tutorial-for-beginners-from-henkels-data-5035ee10a6d2
NEW QUESTION # 30
Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
NEW QUESTION # 31
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 # 32
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 train and register an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auto_mode parameter to key. Deploy the model to the inline endpoint.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 33
......
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