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Microsoft DP-100 is a certification exam that tests the skills and knowledge of candidates who want to design and implement data science solutions on Azure. DP-100 exam is designed to assess the ability of candidates to use Azure technologies to build and deploy machine learning models that can solve real-world problems. Designing and Implementing a Data Science Solution on Azure certification is intended for data scientists and machine learning engineers who want to demonstrate their proficiency in the field of data science.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q161-Q166):

NEW QUESTION # 161
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION # 162
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.
The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.
You need to evaluate the outcome of the early termination policy
What should you evaluate? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 163
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:

Answer:

Explanation:

Explanation

Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
1. To see all compute targets for your workspace, use the following steps:
2. Navigate to Azure Machine Learning studio.
3. Under Manage, select Compute.
4. Select tabs at the top to show each type of compute target.

Box 3: Yes
min_nodes is not specified, so it defaults to 0.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovis
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

NEW QUESTION # 164
You create an Azure Machine Learning workspace and a dataset. The dataset includes age values for a large group of diabetes patients. You use the dp.mean function from the SmartNoise library to calculate the mean of the age value. You store the value in a variable named age.mean.
You must output the value of the interval range of released mean values that will be returned 95 percent of the time.
You need to complete the code.
Which code values should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
To complete the code to output the value of the interval range of released mean values that will be returned 95 percent of the time using the SmartNoise library, you would need to use the privacy_usage_to_accuracy method with a value of 0.95 for the interval percentage. The dp.mean function from the SmartNoise library can return this interval when the required methods and parameters are used correctly.
print(age_mean.privacy_usage_to_accuracy(0.95))
age_mean is the variable that holds the differentially private mean value calculated using the dp.mean function.
privacy_usage_to_accuracy(0.95) method is called on age_mean to get the interval range for 95% confidence.
Therefore, the selections should be:
privacy_usage_to_accuracy
0.95
This will give you the interval range of released mean values that will be returned 95 percent of the time.

NEW QUESTION # 165
You create a new Azure Machine Learning workspace with a compute cluster.
You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

NEW QUESTION # 166
......

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