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| Section | Weight | Objectives |
|---|---|---|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
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NEW QUESTION # 153
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: A
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 # 154
You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
Answer: A
Explanation:
Reproducibility across environments depends on consistent versioning of datasets and environments. Differences in data or dependencies can lead to inconsistent results even if compute resources are identical. Proper asset versioning ensures experiments can be reliably reproduced in any workspace or region.
NEW QUESTION # 155
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage 1. The storage account includes a publicly accessible container named mlcontainer 1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer 1.
You need to complete the Python SDK v2 code.
How should you complete the code? To answer, select the appropriate options in the answer area . NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Because you want to reference all blobs in a container rather than a single file, the correct asset type is AssetTypes.URI_FOLDER. This type points to a directory-level URI, allowing Azure ML to traverse all files within it. For a publicly accessible Azure Blob Storage container, the URI follows the pattern
https://storage_account.blob.core.windows.net/container_name/. You then create the Data object with the correct type, path, name, and version parameters, and register it in the workspace using ml_client.data.
create_or_update. Do not use AssetTypes.URI_FILE, which references a single file. Do not use AssetTypes.
MLTABLE unless you have an MLTable YAML descriptor. URI_FOLDER is the correct choice for referencing a collection of CSV blobs in an Azure Blob Storage container, allowing Azure ML to discover and process all files within the specified path.
Microsoft Learn Reference Topic: Create and manage data assets in Azure Machine Learning Python SDK v2 - URI_FOLDER
NEW QUESTION # 156
Drag and Drop Question
A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
- Retrieve feature values dynamically at inference time.
- Use the same feature definitions that were used during training.
- Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 157
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?
Answer: D
Explanation:
You should use the Hate and Unfairness Evaluator (a specific type of Content Safety Evaluator) in Microsoft Azure AI Foundry. This tool is designed to identify and measure the severity of hateful content toward protected groups, ensuring your assistant aligns with responsible AI standards.
Why Use the Content Safety Evaluator?
Targeted Detection: It identifies language attacking or discriminating against people based on race, religion, gender, and other identity factors.
Severity Scoring: It provides a numerical severity score (often 0-7) to help you understand the level of risk in a response.
Reasoning: It often includes a "reason" or "explanation" column that explains why a specific response was flagged.
Multilingual Support: These models are trained to work across multiple languages, including English, Spanish, French, and Japanese.
Reference:
https://arize.com/blog/evaluating-and-improving-ai-agents-at-scale-with-microsoft-foundry
NEW QUESTION # 158
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