DRAG DROP
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You train and register a model by using the Azure Machine Learning Python SDK v2 on a local workstation. Python 3.7 and Visual Studio Code are installed on the workstation.
When you try to deploy the model into production to a Kubernetes online endpoint, you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
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.
Show Answer
Correct Answer: Install Docker on the workstation.
Run the begin_create_or_update method of an MLClient class instance with the local parameter set to true.
Debug and modify the scoring script as necessary.
Explanation: Local debugging of Azure ML online endpoints uses Docker to run the service container on the workstation. Setting local=True deploys the endpoint locally instead of to Kubernetes, allowing inspection of logs and live testing. Once running locally, the scoring script can be debugged and corrected before redeploying to production.
Question 109
HOTSPOT
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You use an Azure Machine Learning workspace, Azure Data Factory pipeline, and a dataset monitor that runs on a schedule to detect data drift.
You need to implement an automated workflow to trigger when the dataset monitor detects data drift and launch the Azure Data Factory pipeline to update the dataset. The solution must minimize the effort to configure the workflow.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Trigger the workflow:
Azure Event Grid subscription
Implement the workflow:
Azure Logic App
Explanation: Azure Machine Learning dataset drift emits events that can be subscribed to with Event Grid, requiring minimal configuration. A Logic App can natively receive Event Grid events and easily start an Azure Data Factory pipeline with built-in connectors, minimizing implementation effort.
Question 110
You manage an Azure Machine Learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
A. Azure Kubernetes Service (AKS)
B. Azure Machine Learning compute clusters
C. Azure Container Instances (ACI)
D. Local deployment
Show Answer
Correct Answer: B
Explanation: Azure Machine Learning compute clusters support low-priority (spot) VMs, which provide significant pricing discounts and are designed for workloads that can tolerate interruptions. This makes them the correct compute target for deploying a model using discounted, low-priority virtual machines. AKS, ACI, and local deployment do not provide this low-priority VM option in the same way.
Question 111
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?
A. mlflow.log_artifact()
B. mlflow.log.dict()
C. mlflow.log_metric()
D. mlflow.log_text()
Show Answer
Correct Answer: D
Explanation: In Azure Machine Learning SDK v2, experiment tracking uses MLflow. MLflow metrics are strictly numeric, so there is no supported way to log a string as a metric using mlflow.log_metric(). To record string-based information during a run, you log it as text, which is stored as a run artifact. The MLflow method designed for this purpose is mlflow.log_text(), which accepts a string and saves it to a text artifact associated with the run.
Question 112
You manage an Azure Machine Learning workspace.
You build a custom model you must log with MLflow. The custom model includes the following:
• The model is not natively supported by MLflow.
• The model cannot be serialized in Pickle format.
• The model source code is complex.
• The Python library for the model must be packaged with the model.
You need to create a custom model flavor to enable logging with MLflow.
What should you use?
A. model loader
B. artifacts
C. model wrapper
D. custom signatures
Show Answer
Correct Answer: A
Explanation: A custom MLflow model flavor for a complex, non-picklable model with bundled source code and dependencies should use a model loader. A model loader lets you define custom loading and prediction logic, package required artifacts and libraries, and handle models that cannot be serialized with Pickle, which exactly matches the scenario described.
Question 113
You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target, an environment, and a training script.
You need to prepare information to submit a training job.
Which class should you use?
A. MLClient
B. BuildContext
C. EndpointConnection
D. command
Show Answer
Correct Answer: D
Explanation: In Azure Machine Learning SDK v2, the `command` class is used to define and prepare a training job, including the code, environment, compute target, and command to run. This prepared job object is then submitted using `MLClient`. The other options do not represent job configuration for training runs.
Question 114
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 in the review screen.
You train and register an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: Azure Machine Learning online endpoints support key-based or token-based authentication. Setting auth_mode to key enables access using endpoint keys, which are non-expiring artifacts, unlike tokens that expire. Therefore, this solution meets the requirement.
Question 115
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 in the review screen.
You train and register an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution: Create a managed online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The goal requires a non-expiring authentication artifact. Setting auth_mode to aml_token uses Azure ML tokens, which expire. Non-expiring access requires key-based authentication (auth_mode=key). Therefore, the solution does not meet the goal.
Question 116
HOTSPOT
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You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script1.py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script1.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: command
code
Explanation: In Azure ML SDK v2, a training job that runs a Python script is defined using the command() function, and the folder containing the script is specified with the code parameter (./src).
Question 117
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Stop the current kernel.
B. Change the compute.
C. Reset the compute.
D. Navigate to another section of the workspace.
E. Change the current kernel.
Show Answer
Correct Answer: B, C, E
Explanation: Resetting the notebook state in Azure Machine Learning Studio means clearing all variables and execution context. This can be achieved by switching to a different compute (B), which starts a fresh execution environment; resetting the compute (C), which restarts the underlying compute instance; or changing the current kernel (E), which launches a new kernel with no prior state. Stopping the kernel only halts execution, and navigating elsewhere does not reset state.
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