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Exam DP-100 topic 3 question 102 discussion

Actual exam question from Microsoft's DP-100

Question #: 102
Topic #: 3

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You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.
You have the following Python code segment in the Azure Machine Learning workspace: import mlflow import mlflow.azureml import azureml.mlflow import azureml.core from azureml.core import Workspace subscription_id = 'subscription_id' resourse_group = 'resource_group_name' workspace_name = 'workspace_name' ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group) experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" mlflow.set_experiment(experimentName) uri = ws.get_mlflow_tracking_uri() mlflow.set_tracking_uri(uri)
Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
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by TEO96B at Dec. 29, 2021, 11:09 a.m.

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TEO96B

Highly Voted 3 years ago

It should be NYN, shouldn't it?

upvoted 27 times

Lion007

1 year ago

I agree, it should be NYN Box 1: No - This method does not create a new resource group or Azure Machine Learning workspace; it simply accesses an existing one. Box 2: Yes - The get_mlflow_tracking_uri() method retrieves the tracking URI of the Azure Machine Learning workspace, and set_tracking_uri() directs MLflow to send its tracking data to this URI. Box 3: No - The Python code provided does not include any code that specifically sets up tracking for the "epoch loss" metric. While MLflow is capable of tracking such a metric, it would require explicit calls to mlflow.log_metric() within the training loop, which are not present in the provided code segment.

upvoted 2 times

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vish9

Highly Voted 1 year, 7 months ago

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2&tabs=cli%2Cmlflow The above link states: Azure Databricks can be configured to track experiments using MLflow in two ways: Track in both Azure Databricks workspace and Azure Machine Learning workspace (dual-tracking) Track exclusively on Azure Machine Learning By default, dual-tracking is configured for you when you linked your Azure Databricks workspace. Hence It should be NNN

upvoted 6 times

Matt2000

11 months, 1 week ago

I followed your link. The code specified in this question occurs in the section 'Tracking exclusively on Azure Machine Learning workspace'. I suppose that the second question should be 'Yes'.

upvoted 1 times

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Secure_Defense

Most Recent 5 months ago

Should be NNN. For second box: NO "You can configure Azure Databricks to track experiments using MLflow in two ways: - Track in both Azure Databricks workspace and Azure Machine Learning workspace (dual-tracking) - Track exclusively on Azure Machine Learning By default, when you link your Azure Databricks workspace, dual-tracking is configured for you. Linking your Azure Databricks workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace and Azure Databricks workspace at the same time. This configuration is called Dual-tracking. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2&tabs=cli%2Cmlflow#track-azure-databricks-runs-with-mlflow For third box: NO The code doesn't provide any segment to track metrics.

upvoted 1 times

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SunilB

1 year, 9 months ago

Should be NNN After you link your Azure Databricks workspace with your Azure Machine Learning workspace, MLflow Tracking is automatically set to be tracked in all of the following places: The linked Azure Machine Learning workspace. Your original ADB workspace.

upvoted 5 times

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vishal_aiml164

1 year, 12 months ago

It should be N,N,N as i see that 1) we will not create ws 2) in latest Azure ML and Azure DB we can monitor the logs 3) there is no code for logging in mlflow

upvoted 6 times

vishal_aiml164

1 year, 12 months ago

FYR : https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?tabs=cli%2Cmlflow

upvoted 1 times

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ning

2 years, 7 months ago

I do not fully understand this question, in the statements, there are no statement for logging, in order mlflow to log, one the following needs to be called: mlflow.log_param("alpha", alpha) mlflow.log_metric('mse', 1.23) mlflow.log_artifact("actuals_vs_predictions.png") mlflow.log_model(lr, "linear Model") if no statement to log, how would anything to be traced or logged???

upvoted 1 times

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AjoseO

2 years, 10 months ago

On 03 March 2022 The 3rd question was different

upvoted 4 times

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