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Microsoft DP-100 Exam Sample Questions

Question # 1

You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?

A. Fisher Linear Discriminant Analysis.

B. Filter Based Feature Selection

C. Synthetic Minority Oversampling Technique (SMOTE)

D. Permutation Feature Importance

Answer

C. Synthetic Minority Oversampling Technique (SMOTE)

Explanation:

Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.

[Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote, , ]

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Question # 2

You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed. Which statistic should you use?

A. Max

B. Type

C. Profile

D. Mean

Answer

C. Profile

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Question # 3

You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job. Which class should you use?

A. MLClient

B. command

C. BuildContext

D. EndpointConnection

Answer

B. command

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Question # 4

You plan to provision an Azure Machine Learning Basic edition workspace for a data science project. You need to identify the tasks you will be able to perform in the workspace. Which three tasks will you be able to perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Create a Compute Instance and use it to run code in Jupyter notebooks.

B. Create an Azure Kubernetes Service (AKS) inference cluster.

C. Use the designer to train a model by dragging and dropping pre-defined modules.

D. Create a tabular dataset that supports versioning.

E. Use the Automated Machine Learning user interface to train a model.

Answer

A. Create a Compute Instance and use it to run code in Jupyter notebooks.
B. Create an Azure Kubernetes Service (AKS) inference cluster.
D. Create a tabular dataset that supports versioning.

Explanation:

[Reference:, https://azure.microsoft.com/en-us/pricing/details/machine-learning/, , , ]

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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)

Answer

A. run(mini_batch)
D. init()

Explanation:

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

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