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DP-100 Exam Dumps - Microsoft Azure Questions and Answers

Question # 44

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?

Options:

A.

mlflowlog_metrk()

B.

mlflow.log.dict()

C.

mlflow.log text()

D.

mlflow.log_artifact()

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Question # 45

You need to define an evaluation strategy for the crowd sentiment models.

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.

Options:

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Question # 46

You need to configure the Edit Metadata module so that the structure of the datasets match.

Which configuration options should you select? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Options:

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Question # 47

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:

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:

A.

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.

B.

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.

C.

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.

D.

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.

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Question # 48

You have an Azure Machine Learning workspace.

You run the following code in a Python environment in which the configuration file for your workspace has been downloaded.

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.

Options:

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Question # 49

You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?

Options:

A.

run.upload_file(‘row_count’, ‘./data.csv’)

B.

run.log(‘row_count’, rows)

C.

run.tag(‘row_count’, rows)

D.

run.log_table(‘row_count’, rows)

E.

run.log_row(‘row_count’, rows)

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Question # 50

You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.

You develop a training script for the model on a local machine.

You need to load the model fairness metrics into Azure Machine Learning studio.

What should you do?

Options:

A.

Implement the download_dashboard_by_upload_id function

B.

Implement the creace_group_metric_sec function

C.

Implement the upload_dashboard_dictionary function

D.

Upload the training script

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Question # 51

You manage an Azure Machine Learning workspace named workspace1 and a Data Science Virtual Machine (DSVM) named DSMV1.

You must an experiment in DSMV1 by using a Jupiter notebook and Python SDK v2 code. You must store metrics and artifacts in workspace 1 You start by creating Python SCK v2 code to import ail required packages.

You need to implement the Python SOK v2 code to store metrics and article in workspace1.

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 the correctly order.

Options:

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Question # 52

You need to implement a new cost factor scenario for the ad response models as illustrated in the

performance curve exhibit.

Which technique should you use?

Options:

A.

Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.

B.

Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.

C.

Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.

D.

Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.

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Question # 53

You manage an Azure Machine Learning workspace by using the Python SDK v2.

You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.

The cluster definition includes the following properties and values:

• name="mlcluster1’’

• size="STANDARD.DS3.v2"

• min_instances=1

• maxjnstances=4

• tier="dedicated"

The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.

You need to modify the cluster properties to minimize the cost of compute resources.

Which properties should you modify? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Options:

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Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Last Update: Apr 30, 2025
Questions: 460
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