Microsoft passguide microsoft Azure Dp-100 Passing Score by Corey q212 vce pdf

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Exam Name:	Designing and Implementing a Data Science Solution on Azure
Exam Code:	DP-100 Dumps
Vendor:	Microsoft	Certification:	Microsoft Azure
Questions:	476 Q&A's	Shared By:	corey

Question 36

You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.

Model performance has degraded due to model input data changes.

You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.

Which event should you use?

Options:

RunStatusChanged

DatasetDriftDetected

ModelDeployed

RunCompleted

Discussion

Question 37

You have a Python script that executes a pipeline. The script includes the following code:

from azureml.core import Experiment

pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?

Options:

pipeline_run.get.metrics()

pipeline_run.wait_for_completion(show_output=True)

pipeline_param = PipelineParameter(name="stdout",default_value="console")

pipeline_run.get_status()

Discussion

Question 38

A set of CSV files contains sales records. All the CSV files have the same data schema.

Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

Questions 38

At the end of each month, a new folder with that month’s sales file is added to the sales folder.

You plan to use the sales data to train a machine learning model based on the following requirements:

You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.

You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.

You must register the minimum number of datasets possible.

You need to register the sales data as a dataset in Azure Machine Learning service workspace.

What should you do?

Options:

Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing theexisting dataset and specifying a tag named month indicating the month and year it was registered. Usethis dataset for all experiments.

Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.

Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments,identifying the version to be used based on the month tag as necessary.

Discussion

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Question 39

You manage an Azure Machine Learning workspace named workspace!.

You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2.

You must transform the Python code into a YAML specification that can be processed by the pipeline service.

You need to import the Python library that provides the transformation functionality.

Which Python library should you import?

Options:

azure.ai ml.automl

azure.ai.ml.entities

sklearn

mldesigner

Discussion