選擇我們的高質量的DP-100考試題庫：Designing and Implementing a Data Science Solution on Azure，Microsoft DP-100一定會很簡單

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Microsoft DP-100（在Azure上設計和實施數據科學解決方案）認證考試是數據科學專業人員最追求的認證考試之一。它旨在測試您在Azure平台上設計和實施數據科學解決方案的技能和知識。這個認證考試是驗證您在數據科學方面的專業知識和展示您利用Azure技術解決複雜業務問題的能力的絕佳方式。

DP-100考試題庫

Microsoft DP-100證照，DP-100測試題庫

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DP-100考試涵蓋了各種話題，包括數據準備、建模和部署。候選人需要展示他們使用各種Azure數據服務的能力，例如Azure機器學習、Azure Databricks和Azure HDInsight。他們還需要展示他們在編程語言，如Python和R上的熟練程度。

最新的 Microsoft Azure DP-100 免費考試真題 (Q233-Q238):

問題 #233
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 tin- 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 Principal Components Analysis (PCA) sampling mode.
Does the solution meet the goal?

A. No
B. Yes

答案：A

解題說明：
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.
Incorrect Answers:
The Principal Component Analysis module in Azure Machine Learning Studio (classic) is used to reduce the dimensionality of your training data. The module analyzes your data and creates a reduced feature set that captures all the information contained in the dataset, but in a smaller number of features.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/principal-component-analysis

問題 #234
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.

答案：

解題說明：

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

問題 #235
You create a binary classification model using Azure Machine Learning Studio.
You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
You need to create the required business metrics.
How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

問題 #236
You register a file dataset named csv_folder that references a folder. The folder includes multiple comma- separated values (CSV) files in an Azure storage blob container.
You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references.
Which code segment should you insert to replace the code comment?

A. inputs=[file_dataset.as_named_input('training_files')],
B. script_params={'--training_files': file_dataset},
C. inputs=[file_dataset.as_named_input('training_files').to_pandas_dataframe()],
D. inputs=[file_dataset.as_named_input('training_files').as_mount()],

答案：D

解題說明：
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

問題 #237
You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.
You must use CUDA-based model training.
You need to provision the Compute Instance.
Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area.
NOTE: Each correct selection is worth one point.