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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 2: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
For the Operationalizing Machine Learning and Generative AI Solutions (AI-300) web-based practice exam no special software installation is required. Because it is a browser-based Operationalizing Machine Learning and Generative AI Solutions (AI-300) practice test. The web-based Operationalizing Machine Learning and Generative AI Solutions (AI-300) practice exam works on all operating systems like Mac, Linux, iOS, Android, and Windows.
NEW QUESTION # 60
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 # 61
Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
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 # 62
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
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:
To host generative AI workloads in a Microsoft Foundry environment with strictly private communication and no public network exposure, you must configure a Managed Virtual Network (Managed VNet) with specific isolation settings and disable all public inbound access.
[A]
Enable Managed Virtual Network Isolation
During the creation of your Azure AI Foundry hub, navigate to the Networking tab.
Select the Private with Approved Outbound isolation mode. This mode ensures that all outbound traffic from the managed compute resources is restricted to only the destinations you explicitly approve, such as dependent Azure resources.
Once enabled, this isolation mode cannot be disabled.
[E]
Disable Public Inbound Access
In the Networking tab of your Foundry resource, set Public network access to Disabled.
This action blocks all traffic from the public internet, ensuring the resource is only accessible through private connections.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/managed-virtual-network
NEW QUESTION # 63
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: A
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 64
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
[D]
To evaluate a trained loan classification model for Responsible AI expectations--ensuring no disproportionate impact on protected groups, evaluating error across segments, and verifying prediction transparency--you can employ SHAP (SHapley Additive exPlanations) values to assess feature importance.
This approach allows you to identify which variables (e.g., credit history, debt levels) drive the model's predictions, fostering trust and fairness.
Feature Importance for Transparency: Use SHAP (model-agnostic) or LIME (local approximations) to explain why the model approved or denied a loan. These techniques identify how each feature contributes to individual predictions.
[E]
To ensure a trained loan approval classification model meets responsible AI expectations-- specifically, that it does not disproportionately impact protected groups and that errors can be evaluated across segments--you should analyze error rates across defined demographic cohorts using Fairness-Aware Machine Learning metrics.
Reference:
https://urfpublishers.com/journal/artificial-intelligence/article/view/explainable-aiml-testing- ensuring-transparency-accountability-and-compliance
https://timvero.com/blog/ethics-in-automated-lending-can-ai-make-fair-credit-decisions
NEW QUESTION # 65
......
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