Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Exam Questions

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Total 506 questions
Question 1

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 are creating a new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than the other classes in the training set.

You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution: You use the Scale and Reduce sampling mode.

Does the solution meet the goal?



Answer : B

Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Note: SMOTE is used 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.


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

Question 2

You are developing a hands-on workshop to introduce Docker for Windows to attendees.

You need to ensure that workshop attendees can install Docker on their devices.

Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.



Answer : C, E

C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.

Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.


https://docs.docker.com/toolbox/toolbox_install_windows/

https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

Question 3

You have a Python script that executes a pipeline. The script includes the following code:

from azureml.core import Experiment

pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?



Answer : B

wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.

Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)

Parameter: show_output

Indicates whether to show the run output on sys.stdout.


Question 4

You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.

What should you create?



Answer : E


Question 5

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?



Answer : B

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'

)


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

Question 6

You train and register an Azure Machine Learning model

You plan to deploy the model to an online endpoint

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution:

Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.

Does the solution meet the goal?



Answer : B


Question 7

You use the Azure Machine learning SDK foe Python to create a pipeline that includes the following step:

The output of the step run must be cached and reused on subsequent runs when the source.directory value has not changed.

You need to define the step.

What should you include in the step definition?



Answer : A


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