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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Security and governance
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Design and implement generative AI solutions | - Large language model integration
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
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질문 # 21
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement.
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
In Azure ML command jobs, MLflow automatically creates a run context, so explicit start_run is optional.
The training script must call mlflow.log_param to record each hyperparameter for the run. If the script calls this for each hyperparameter, the requirement for capturing parameters per run is met. The script must also call mlflow.log_metric to record numeric metrics such as accuracy or loss, optionally indexed by training step. Metrics and parameters logged inside an Azure ML command job are automatically associated with the correct experiment and run - no manual specification of experiment or run ID is required. If all three elements (run context, parameter logging, metric logging) are present in the script, all experiment tracking statements are True. If any element is absent, the corresponding statement is False and the tracking requirement is not met.
Microsoft Learn Reference Topic: Log metrics, parameters, and artifacts during Azure Machine Learning training runs with MLflow
질문 # 22
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need to implement the method to log the string metrics. Which method should you use?
정답：C
질문 # 23
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?
정답：C
설명：
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
질문 # 24
You manage an Azure Machine Learning workspace.
You choose the uri_folder data type as an output of a pipeline component.
You need to define the data access mode that is supported by your configuration.
Which mode should you define?
정답：C
질문 # 25
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file.
The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
정답：A
설명：
Azure Machine Learning supports a Table type (mltable). This allows for the creation of a blueprint that defines how to load data files into memory as a Pandas or Spark data frame.
Authoring MLTable Files
To directly create the MLTable file, we recommend that you use the mltable Python SDK to author your MLTable files instead of a text editor.
Supported file types
You can create an MLTable using a range of different file types:
* Delimited Text
(for example, CSV files)
from_delimited_files(paths=[path])
Incorrect:
* Parquet
from_parquet_files(paths=[path])
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable
질문 # 26
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