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Microsoft DP-100 certification exam is a comprehensive assessment of the candidate's knowledge and expertise in the field of data science. DP-100 exam covers a wide range of topics, including data exploration and preparation, modeling, feature engineering, training and tuning models, and deploying and managing models in Microsoft Azure. DP-100 Exam is designed to test the candidate's ability to design and implement data science solutions using Microsoft Azure data services, including Azure Machine Learning, Azure Databricks, and Azure HDInsight.

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To pass the Microsoft DP-100 exam on the first try, candidates need Designing and Implementing a Data Science Solution on Azure updated practice material. Preparing with real DP-100 exam questions is one of the finest strategies for cracking the exam in one go. Students who study with Microsoft DP-100 Real Questions are more prepared for the exam, increasing their chances of succeeding.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q101-Q106):

NEW QUESTION # 101
You need to modify the inputs for the global penalty event model to address the bias and variance issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

NEW QUESTION # 102
You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.
You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

NEW QUESTION # 103
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:

You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

NEW QUESTION # 104
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

NEW QUESTION # 105
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Augment the data
2 - Add the Bayesian Linear Regression module.
3 - Configure the regularization weight.

NEW QUESTION # 106
......

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