Microsoft DP-100 Exam Questions


Vendor Name: Microsoft
Certification Name:Microsoft Azure
Exam Name:Designing and Implementing a Data Science Solution on Azure Exam

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Microsoft DP-100 Exam Questions


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DP-100 Exam Sample Questions:



Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model
by using scikit-learn. The script includes code to load a training data file which is also located in the scripts
folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model
training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

 

Yes

 

No


No


Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It
is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn





You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B 
The variation of Class B is very high.
You need to resolve imbalances.
Which method should you use?

 

Partition and Sample

 

Cluster Centroids

 

Tomek links

 

Synthetic Minority Oversampling Technique (SMOTE)


Synthetic Minority Oversampling Technique (SMOTE)






You create an Azure Machine Learning workspace and set up a development environment. You plan to train a
deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training
scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target
configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.





You have a dataset that contains 2,000 rows. You are building a machine learning classification model by
using Azure Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
Divide the data into subsets
Assign the rows into folds using a round-robin method
Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point





You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?

 

Create a Data Science Virtual Machine (DSVM) Windows edition.

 

Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.

 

Create a Deep Learning Virtual Machine (DLVM) Linux edition.

 

Create a Deep Learning Virtual Machine (DLVM) Windows edition.

 

Create a Data Science Virtual Machine (DSVM) Linux edition.


Create a Data Science Virtual Machine (DSVM) Linux edition.




How to Pass Microsoft DP-100 Exam?