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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - 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 |
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - 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 |
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NEW QUESTION # 109
Drag and Drop Question
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible AI (RAI) dashboard for model1.
Which three 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.
Answer:
Explanation:
Explanation:
To setup the Responsible AI (RAI) dashboard in Azure Machine Learning and specifically analyze minimal feature modifications needed to change a prediction result, you must use Counterfactual Analysis.
Here are the specific sequential steps you need to take:
Step 1: Load and configure the Responsible AI Insights dashboard constructor component.
Initialize the RAI Insights Dashboard Constructor
1. Create the root pipeline component that acts as the container for your tools.
2. Call the RAI Insights dashboard constructor component.
3. Pass your registered model and your test dataset as the mandatory inputs to this component.
Step 2: Add the Counterfactuals to Responsible AI Insights dashboard
Configure Counterfactual Analysis
1. Add the specific component required to identify what features to modify for a differing prediction.
2. Call the Add Counterfactuals to RAI Insights dashboard component.
3. Link its input to the output of the constructor component initialized in Step 1 above.
4. Configure the parameter variables, including the number of counterfactual examples you want to generate per data point.
Step 3: Use the Gather Responsible AI Insights dashboard component to present the dashboard.
Assemble and Submit the Dashboard Pipeline
1. Gather the components into a cohesive Azure ML pipeline job to execute them.
-> 2. Call the Gather RAI Insights dashboard component to aggregate the constructor and the counterfactual tool outputs.
3. Submit the pipeline job to your Azure Machine Learning workspace compute cluster for processing.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-implement-responsible-ai-dashboards-in-azure-machine-learning/view
NEW QUESTION # 110
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?
Answer: D
Explanation:
The first step you must take is to register the model with the workspace.
To access the no-code, guided wizard for generating a Responsible AI dashboard directly within the Azure Machine Learning studio UI, the trained model must first exist as a recognized asset inside your workspace's model registry.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard
NEW QUESTION # 111
A company plans to deploy a foundation model in Microsoft Foundry.
The mode must support the following workloads:
A customer support workload used across multiple regions
A marketing workload that must remain within a specific region due to data residency requirements You need to select the deployment type.
Which deployment type should you use for each workload? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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:
Explanation:
For a customer support workload used across multiple regions, Global Standard deployment is the right choice: it routes each request to the nearest available Azure region automatically, reducing latency globally and providing the highest throughput and availability. For a marketing workload that must remain within a specific region due to data residency requirements, a Data Zone Standard or single-region deployment ensures all compute and data processing occurs within a defined geographic boundary, satisfying GDPR and local data sovereignty rules. Microsoft Foundry ' s deployment types are designed around exactly this trade-off:
Global routing for performance-critical multi-region workloads, and Data Zone or Regional isolation for data- residency-constrained workloads. Choosing the wrong deployment type can result in either compliance violations or unnecessary latency.
Microsoft Learn Reference Topic: Model deployment options in Microsoft Foundry - Global, Data Zone, and Regional deployment types
NEW QUESTION # 112
You manage an Azure Machine Learning workspace.
You must set up an event-driven process to trigger a retraining pipeline.
You need to configure an Azure service that will trigger a retraining pipeline in response to data drift in Azure Machine Learning datasets. Which Azure service should you use?
Answer: D
NEW QUESTION # 113
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
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:
NEW QUESTION # 114
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