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Microsoft DP-100 certification exam is a comprehensive exam that covers a wide range of topics related to data science solutions on Azure. DP-100 exam consists of multiple-choice questions and requires the candidate to demonstrate their knowledge and understanding of data science concepts and Azure data science solutions. DP-100 Exam is designed to test the candidate’s ability to design and implement data science solutions on Azure.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q291-Q296):

NEW QUESTION # 291
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.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

NEW QUESTION # 292
You manage an Azure Machine Learning workspace.
You plan to import data from Azure Data Lake Storage Gen2.
You need to build a URI that represents the storage location.
Which protocol should you use?

A. https
B. adl
C. abfss
D. wasbs

Answer: C

NEW QUESTION # 293

You need to obtain the output from the pipeline execution. Where will you find the output?

A. the Activity Log in the Azure portal for the Machine Learning workspace
B. the Inference Clusters tab in Machine Learning studio
C. a file named parallel_run_step.txt located in the output folder
D. the debug log
E. the digitjdentification.py script

Answer: C

Explanation:
Explanation
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and
'summary_only'.
'append_row' - All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parall

NEW QUESTION # 294
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma-separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

Answer: C

Explanation:
Explanation
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

NEW QUESTION # 295
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 296
......

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