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| Section | Objectives |
|---|---|
| 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 | - 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 | - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
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NEW QUESTION # 37
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
Answer: B
Explanation:
The best compute target for deploying a model using low-priority VMs (or their modern successor, Spot VMs) is an Azure Machine Learning compute cluster.
Best Compute Target: AML Compute Cluster
For low-priority/Spot pricing, you should use an Azure Machine Learning compute cluster configured with the LowPriority tier.
Primary Use Case: This target is specifically recommended for batch deployments. Batch inference is ideal for low-priority VMs because these jobs are asynchronous and can tolerate the interruptions (preemptions) inherent to discounted capacity.
Pricing Advantage: Low-priority VMs offer significant discounts-often up to 80% off standard rates-by utilizing unused Azure capacity.
Automatic Handling: When a node is preempted during a batch job, Azure Machine Learning automatically attempts to replace the lost capacity and re-queues failed tasks to the cluster.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch
NEW QUESTION # 38
Hotspot Question
A team is provisioning a new Azure Machine Learning workspace for a production project.
The workspace must support secure secret storage and operational monitoring. The team requires the workspace to be created with the correct dependent resources to meet security and monitoring requirements.
You need to configure the required dependencies when the team creates the workspace.
Which resources should you associate with the workspace? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 39
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 action is to register the model in the Azure Machine Learning workspace . Microsoft's documented no-code workflow for generating Responsible AI insights in Azure Machine Learning studio explicitly starts by registering the model. After registration, you navigate to Models , select the registered model, and choose Create Responsible AI dashboard .
The scenario already specifies an MLflow-formatted regression model using tabular data , which aligns with the Responsible AI dashboard's supported model architecture. Microsoft states that models used by the Responsible AI components must be registered in Azure Machine Learning in MLflow format with an sklearn flavor , and the associated datasets must use supported tabular formats such as MLTable.
Option B is incorrect because model explanations are an optional dashboard component configured after selecting the registered model; they are not the prerequisite first step. Option C is unnecessary and counterproductive because MLflow is a supported format. Option D is also unnecessary: a Responsible AI dashboard evaluates a registered model and does not require deployment to a managed online endpoint first.
Microsoft also describes Responsible AI dashboards as being linked directly to registered models in the model registry.
Study Guide Reference: Implement machine learning model lifecycle and operations - model registration, MLflow model management, Responsible AI dashboards, model evaluation, and Azure Machine Learning studio.
NEW QUESTION # 40
Drag and Drop Question
A team deploys a machine learning model to production and monitors it continuously. Alerts are configured on performance and data quality metrics.
Multiple alerts are triggered during normal operation.
You need to perform the appropriate action for each model alert condition.
Which action should you perform for each alert condition? To answer, move the appropriate actions to the correct model alert conditions. You may use each action 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 # 41
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: B
NEW QUESTION # 42
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