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New Microsoft Azure DP-100 Exam Practice Questions

Total 428 Questions

Last Updated On: 3-Oct-2024

Question # 1

You are producing a multiple linear regression model in Azure Machine Learning Studio.
Several independent variables are highly correlated. You need to select appropriate methods for conducting effective feature engineering on all the data. 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.

Question # 2

Question # 3

You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
• Reduce the number of training epochs.
• Reduce the size of the neural network.
• Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area.
NOTE: Each correct selection is worth one point

Question # 4

You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages. You must use MLflow as the logging package for tracking metrics generated in the experiment. You need to complete the script for the experiment.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question # 5

You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data. You need to create the inferencing script for the ParallelRunStep pipeline step. Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A. run(mini_batch)
B. main()
C. batch()
D. init()
E. score(mini_batch)

run(mini_batch)

init()

Reference:
https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelines/parallel

Question # 6

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 easilyconverted 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?
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 the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this 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.

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.

Specify the path.
Example:
The following code gets the workspace existing workspace and the desired datastore by name. And then passes
the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.
from azureml.core import Workspace, Datastore, Dataset
datastore_name = 'your datastore name'
# get existing workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name
datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore
datastore_paths = [(datastore, 'weather/2018/11.csv'),
(datastore, 'weather/2018/12.csv'),
(datastore, 'weather/2019/*.csv')]
weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)

Question # 7

You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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