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| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets |
| 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 |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding |
| 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 |
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NEW QUESTION # 31
Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 32
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
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:
Correct sequence:
* Install the Foundry SDK project client locally.
* Configure safety evaluators.
* Submit an evaluation to the Microsoft Foundry project in the cloud.
The first step is to install and configure the Microsoft Foundry SDK project client . Microsoft documents the Foundry project client as the programmatic entry point for authenticating to a Foundry project and accessing its evaluation capabilities. The client typically uses DefaultAzureCredential, avoiding embedded credentials while enabling access to project resources.
Next, configure the safety evaluators that correspond to the risks that must be measured. Microsoft Foundry provides built-in safety evaluators for categories including Violence, Sexual content, Self-harm, and Hate
/Unfairness . These evaluators analyze generated responses and return structured safety assessments rather than relying on subjective manual review.
Finally, submit the evaluation to the Microsoft Foundry project in the cloud . Current Foundry evaluation workflows define the evaluator configuration, create an evaluation, and start an evaluation run in the project.
Results are persisted in Foundry for comparison, auditing, and CI/CD quality gates.
Uploading evaluation data is unnecessary because the scenario explicitly states that the dataset is already registered . Microsoft documentation specifically instructs users to skip dataset upload when a registered dataset already exists. Free-form text upload is also inappropriate because structured evaluation datasets use supported schemas such as JSONL or CSV.
Study Guide Reference: Implement generative AI quality assurance and observability - Foundry evaluations, safety evaluators, evaluation datasets, cloud evaluation runs, and harmful-content measurement.
NEW QUESTION # 33
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
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:
Explanation:
How should you complete the code?
Box 1: container_name
container_name specifies the parameter name used in the AzureBlobDatastore constructor to identify your target blob storage container.
Box 2: wasbs
Correct Code Formats
Depending on your preference for the storage connection protocol, your completed line of code should look like one of the following variations:
Using the standard HTTPS protocol (Default).
-> Using the WASBS (Windows Azure Storage Blob Secure).
Reference:
https://stackoverflow.com/questions/75275875/create-an-azureblobdatastore-with-sdk-v2
NEW QUESTION # 34
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 # 35
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?
Answer: B
Explanation:
To improve the sampling convergence and effectiveness of your Bayesian hyperparameter sweep, you must decrease the value of max_concurrent_trials (for example, setting max_concurrent_trials = 4).
Decrease max_concurrent_trials
Bayesian sampling is a fundamentally sequential algorithm. It relies entirely on the outcomes of previously completed trials to calculate and select the next optimized set of hyperparameters.
The Problem: Setting max_concurrent_trials = 10 forces the optimization engine to kick off 10 trials at the exact same time. Because none of these 10 overlapping trials have completed or generated metrics yet, they cannot learn from one another. This high parallelism compromises the intelligence of the algorithm, turning your structured Bayesian sweep into a less effective random sampling process.
The Solution: Reducing max_concurrent_trials to a smaller number (such as 4) allows sets of trials to complete sequentially. This ensures the optimizer captures enough historical performance data to intelligently direct subsequent runs, achieving significantly better convergence.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 36
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