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NEW QUESTION 52
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 are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply an Equal Width with Custom Start and Stop binning mode.
Does the solution meet the goal?

A. YesB. No

Answer: B

Explanation:
Use the Entropy MDL binning mode which has a target column.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

 

NEW QUESTION 53
You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset Which property should you set?
A)

B)

C)

D)

A. Option DB. Option CC. Option BD. Option A

Answer: D

Explanation:
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb

 

NEW QUESTION 54
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?

A. service.get_logs()B. service.serialize()C. service.update_deployment_state()D. service.state

Answer: A

Explanation:
You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.
# if you already have the service object handy
print(service.get_logs())
# if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices['mysvc'].get_logs()) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

 

NEW QUESTION 55
You use the following code to define the steps for a pipeline:
from azureml.core import Workspace, Experiment, Run
from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep
ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", ...)
step2 = PythonScriptsStep(name="step2", ...)
pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
experiment = Experiment(workspace=ws,
name='pipeline-experiment')
run = experiment.submit(pipeline)B. run = Run(pipeline_steps)C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
run = pipeline.submit(experiment_name='pipeline-experiment')D. experiment = Experiment(workspace=ws,
name='pipeline-experiment')
run = experiment.submit(config=pipeline_steps)

Answer: A,C

Explanation:
After you define your steps, you build the pipeline by using some or all of those steps.
# Build the pipeline. Example:
pipeline1 = Pipeline(workspace=ws, steps=[compare_models])
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines

 

NEW QUESTION 56
......

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