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Valid Microsoft DP-100 Questions - Prepare Effectively For Exam

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Microsoft DP-100 certification exam is designed for data scientists and data engineers who want to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam is intended for individuals who have experience using Azure to build and deploy machine learning models, as well as those who have experience with data storage and processing on Azure.

Microsoft DP-100 Certification Exam covers a wide range of topics related to data science, including data exploration and visualization, data preparation, modeling, and deployment. It also covers various Azure tools and services for data science, such as Azure Machine Learning, Azure Databricks, Azure Stream Analytics, and more. DP-100 Exam is designed to test your ability to design and implement end-to-end data science solutions on the Azure platform.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q135-Q140):

NEW QUESTION # 135
You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
You need to configure store1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

NEW QUESTION # 136
You manage an Azure Al Foundry project.
You plan to fine-tune a base model by using pre-uploaded training and validation data. You must specify a hyperparameter to ensure the job is reproducible.
You need to submit the fine-tuning training job.
How should you complete the Python code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 137
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Positive skew
Positive skew values means the distribution is skewed to the right.
Box 2: Negative skew
Negative skewness values mean the distribution is skewed to the left.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary- statistics

NEW QUESTION # 138
You train classification and regression models by using automated machine learning.
You must evaluate automated machine learning experiment results. The results include how a classification model is making systematic errors in its predictions and the relationship between the target feature and the regression model's predictions. You must use charts generated by automated machine learning.
You need to choose a chart type for each model type.
Which chart types should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
See below image

NEW QUESTION # 139
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 # 140
......

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