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| Section | Objectives |
|---|---|
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| 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 |
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NEW QUESTION # 110
A company has multiple data science teams working on separate machine learning projects.
The company requires models to be auditable, reusable, and governed centrally across teams.
The models must allow team-level isolation for billing.
You need to establish the foundation for governed machine learning operations.
Which action should you perform first?
Answer: D
Explanation:
The ideal first step is to implement a Hub-and-Spoke Workspace Architecture combined with Azure Machine Learning Registries.
This hybrid blueprint natively decouples your organizational boundaries (billing and access) from your machine learning assets (models, environments, and pipelines).
In enterprise-scale machine learning, an Azure Machine Learning Hub Workspace acts as a central governance plane. It lets IT administrators configure security, shared compute resources, networks, and compliance guidelines centrally once. From this central hub, different teams can spin up isolated project workspaces. This provides strict data and team-level separation, which naturally supports precise cost tracking and billing isolation per team.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-hub-workspace
NEW QUESTION # 111
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 on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.
The training_data argument specifies the path to the training data in a file named dataset 1. csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset 1. csv
Does the solution meet the goal?
Answer: B
Explanation:
This solution attempts to pass the dataset filename as a positional argument, bypassing the entire Azure Machine Learning input-binding mechanism. In Azure ML command jobs, data assets are not simple local file paths - they are managed references to data stored in Azure Storage that Azure ML resolves and mounts or downloads to the compute target at runtime. The script cannot directly reference dataset1.csv as a bare filename because there is no guarantee that file will be in the working directory of the compute target.
Moreover, the script ' s argparse configuration uses --training_data as a named parameter, not a positional argument, so the command syntax does not match the expected interface. The correct approach uses the input binding placeholder syntax, which instructs Azure ML to resolve the registered data asset and provide its local path to the script at runtime.
Microsoft Learn Reference Topic: Work with data in Azure Machine Learning command jobs - Input binding syntax
NEW QUESTION # 112
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. NOTE: Each correct selection is worth one point. Choose two .
Answer: A,C
Explanation:
Microsoft ' s Azure AI Foundry documentation describes two required configurations for fully private network isolation. First, configure a managed virtual network for the Foundry resource: this provisions a Microsoft-managed VNet that encapsulates all internal service communications - the Foundry control plane communicates with dependent Azure services such as storage, key vault, and cognitive services over private endpoints within this managed VNet, so no traffic crosses the public internet. Second, disable public network access to the Foundry resource: this removes the public endpoint entirely, ensuring that only clients on approved private networks via private endpoint connection can reach it. Together, these two actions ensure all traffic remains on private networks with no public endpoints available, satisfying the security auditor requirement for complete network isolation.
Microsoft Learn Reference Topic: Configure managed virtual network for Azure AI Foundry - Network isolation and private endpoints
NEW QUESTION # 113
You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?
Answer: A
Explanation:
When labeled data is unavailable, traditional accuracy metrics cannot be computed. Data drift detection monitors changes in input data distribution, serving as a proxy for potential performance degradation. This allows early detection of issues before labeled data becomes available.
NEW QUESTION # 114
Drag and Drop Question
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. 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 # 115
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