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| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Topic 2: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 3: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
| Topic 4: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 5: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
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NEW QUESTION # 139
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 # 140
You have an Azure Machine Learning workspace.
You have the following code:
You plan to rely on serverless compute to train a model by using Azure Machine Learning Python SDK v2.
The serverless compute must use a designated number of nodes of a specific virtual machine type.
You need to modify the code to run the training job according to the plan.
How should you modify the command object? To answer, select the appropriate oations in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 141
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
LLM model: GPT-4o
Deployment type: Global batch
GPT-4o is the correct model because it is a multimodal model capable of processing both text and image inputs while generating text-based responses. This directly satisfies the requirement to generate content based on a combination of text and images. DALL-E is primarily designed for image generation rather than general multimodal language processing, while Whisper is designed for speech recognition and audio transcription.
Global batch is the correct deployment type because Microsoft specifically positions it for large-scale, high- volume asynchronous processing . Global Batch accepts large groups of requests and processes them using a separate enqueued-token quota. Critically, Microsoft states that this separate quota helps ensure that batch processing does not disrupt online workloads , which directly matches the scenario. It also provides a target turnaround of approximately 24 hours and is optimized for use cases such as large-scale content generation.
Standard deployments are better suited to interactive or lower-volume inference. Global provisioned deployments provide reserved predictable throughput, but they do not provide the specific workload isolation benefit described for asynchronous Global Batch processing.
Study Guide Reference: Design and implement a GenAIOps infrastructure - multimodal model selection, Azure OpenAI deployment types, batch inference, throughput management, and workload isolation.
NEW QUESTION # 142
Drag and Drop Question
A team is deploying a new version of a customer scoring model to a production online endpoint in Azure Machine Learning.
The team must minimize risk by gradually introducing the new model version and ensuring that traffic can be reverted immediately if issues occur.
You need to roll out the new model according to the requirements.
Which four 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 safely deploy your new customer scoring model with minimal risk, you should use Azure Machine Learning managed online endpoints with a blue-green deployment strategy. This allows you to test the new model (green) alongside the old model (blue) and route traffic gradually.
Here is the step-by-step process to execute this rollout.
Step 1: Create a New Deployment for the updated model version.
1. Create a New Deployment under the Existing Endpoint
Do not overwrite your current production deployment. Instead, deploy the new model version as a separate deployment within the same managed online endpoint.
Keep the existing deployment (e.g., blue-deployment) running at 100% traffic.
Create the new deployment (e.g., green-deployment) with the new model version.
Set the initial traffic allocation for the new deployment to 0%.
2. Test the New Deployment in Isolation [Step not in this question]
Before exposing real customers to the new model, test its stability and performance in the production environment.
Send test scoring requests directly to the green-deployment.
Use the X-MS-Routing-Override: green-deployment HTTP header in your SDK or REST requests to bypass the traffic split and target the new model directly.
Verify that the data schema, latency, and prediction outputs match your expectations.
Step 2: Configure traffic split to route a small percentage to the new deployment.
3. Gradually Shift Traffic (Canary Testing)
Once internal testing passes, begin introducing real production traffic to the new model in phases.
3a. Update the endpoint's traffic configuration to route a small percentage (e.g., 10%) to green- deployment. [Step 2] Step 3: Monitor endpoint metrics and error rates.
3b) Monitor system metrics closely, looking for spikes in HTTP 4xx/5xx errors, timeouts, or unusual scoring distributions. [Step 3] Step 4: Increase traffic to the new deployment after validation
3c) If performance remains stable over a designated observation period, incrementally increase the traffic (e.g., to 30%, 50%, then 100%). [Step 4] Further future actions outside the context of the question:
4. Monitor App Insights and MetricsAzure ML integrates natively with Azure Monitor and Application Insights. Track these critical metrics during the shift:CPU/Memory Utilization: Ensure the new model does not exhaust container resources.
Model Latency: Compare the response times of the new deployment against the old one.
Tracking Token/Feature Drift: Ensure the live customer data matches the model's expected input feature distribution.
5. Instant Rollback Plan
If the new model causes errors, triggers high latency, or skews customer scores, you can revert traffic immediately.
Update the endpoint traffic settings to route 100% of traffic back to the original blue-deployment.
Set the green-deployment traffic to 0%.This configuration change takes effect almost instantly, isolating the faulty model without any downtime for your users.
Keep the green-deployment alive at 0% traffic while you pull the logs to investigate the root cause, then delete it once fixed.
Incorrect:
[Remove the previous deployment]
A step further in the future.
Reference:
https://chandras4bigdata.medium.com/mlops-interview-and-study-guide-489d13ff82fa
NEW QUESTION # 143
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
Answer: D
Explanation:
A GitHub Actions workflow can absolutely be configured to automatically evaluate pull requests using Azure AI Foundry and fail the CI build if safety thresholds are exceeded.By integrating the Azure AI Evaluation SDK into your automated testing suite, you can scan agentic applications for safety risks (like jailbreaks, hate speech, or content harms) during the continuous integration (CI) lifecycle.
Reference:
https://medium.com/@cataldi.ricardo/compliance-and-governance-app-with-pyrit-39526bd2e4f8
NEW QUESTION # 144
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