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 a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AksWebservice instance.
Set the value of the auth_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?

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.

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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,M,f2,l
1,1,2,0
2,1,1,1
32,10
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.
You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?