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| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails |
| Implement generative AI quality assurance and observability | - 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 - Conduct red teaming, adversarial testing, and content filtering |
| Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - 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 |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - 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 |
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NEW QUESTION # 102
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: D
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 # 103
Drag and Drop Question
You manage an Microsoft Foundry project.
You deploy a large language model from the model catalog.
You need to manually evaluate the model, collect the statistics, and be able to review the results later.
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:
Step 1: Import Data in CSV Format
Supported formats: Azure AI Studio natively accepts .csv and .jsonl files for evaluation datasets.
Requirement: Your file must contain the input columns (e.g., user prompts) that you want to test against the model.
Step 2: Evaluate the Solution on 50 input Rows
Sample size: 50 rows is an excellent size for a manual, qualitative test baseline.
Execution: You will upload this dataset into the Evaluation blade of your project and map your data fields to the model's inputs.
Step 3: Provide thumbs up or down ratings to model responses
Manual UI: The platform features a manual review interface (often called human-in-the-loop evaluation).
Feedback: You can view the model's generated response for each of the 50 rows side-by-side with the input and log your binary (thumbs up/down) or detailed feedback.
Step 4: Save the evaluation results
Persistence: Once completed, the session is saved to your project's run history.
Review: You can return to the dashboard later to view aggregate statistics, check pass/fail rates, and export the annotated data for your records.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-perform-azure-migrate-at-scale-using-csv-import-for-large-datacenter-inventories/view
NEW QUESTION # 104
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
Answer: C
Explanation:
To restore the service as quickly as possible, you can roll back traffic to the previous deployment by updating the traffic allocation settings of your Azure Machine Learning online endpoint.
Azure Machine Learning managed online endpoints support multiple deployments under a single endpoint, allowing for blue-green deployment strategies where you can shift traffic between versions instantly.
Key Benefits of This Approach
Instant Recovery: Traffic shifting is a routing change and does not require redeploying the previous model's code or environment, making it the fastest recovery method.
No Downtime: Because the previous deployment remains "warm" (active but receiving no traffic), the switch happens without interrupting the service.
Isolation for Debugging: You can keep the failing deployment at 0% traffic to inspect its logs using az ml online-deployment get-logs without affecting end users How to Roll Back Traffic If your new deployment (e.g., "green") is returning errors, you can reallocate 100% of the traffic back to the known stable deployment (e.g., "blue") using the following methods:
* Azure CLI: Use the az ml online-endpoint update command to set the traffic percentage:
az ml online-endpoint update --name <your-endpoint-name> --traffic "blue=100 green=0"
* Azure Machine Learning Studio:
Navigate to Endpoints in the left menu.
Select your specific real-time endpoint.
Go to the Details or Live Traffic tab.
Adjust the traffic percentages so the previous deployment receives 100% and the failing deployment receives 0%.
Select Update or Save to apply the changes immediately
Reference:
https://learn.microsoft.com/en-us/answers/questions/1275110/azure-ml-v2-yaml-code-for-live- traffic-allocation
NEW QUESTION # 105
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 106
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace. The component definition is stored in the local file ./components/train/train.yml.
You write code to connect to the workspace by using the ml_client object and import all required libraries.
You need to complete the remaining code.
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:
Box 1: load_component
train_component
Example:
Create component: training (using yaml definition)
Create and register the component. Registering it allows you to re-use it in other pipelines. Also, anyone else with access to your workspace can use the registered component.
# importing the Component Package
from azure.ai.ml import load_component
# Loading the component from the yml file
train_component = load_component(source=os.path.join(train_src_dir, "train.yml"))
# Now we register the component to the workspace
train_component = ml_client.create_or_update(train_component)
# Create (register) the component in your workspace
print(
f"Component {train_component.name} with Version {train_component.version} is registered" ) Box 2: create_or_update ml_client Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-python-sdk
NEW QUESTION # 107
......
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