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DP-100 Designing and Implementing a Data Science Solution on Azureは、技術的な精度の最高水準を高め、認定された主題と専門家のみを使用します。最新の正確なDP-100試験トレントをクライアントに提供し、提供する質問と回答は実際の試験に基づいています。合格率が高く、約98％-100％であることをお約束します。また、DP-100テストブレインダンプは高いヒット率を高め、試験を刺激してDP-100試験の準備を整えることができます。あなたの成功は、DP-100試験問題に縛られています。

Microsoft DP-100 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Available Languages:Japanese, Chinese (Simplified), French, Portuguese, English, Spanish, German, Korean
Passing Score:700/1000
Certificate Validity Period:1 year
Real Exam Qty:40-60
Exam Format:Interactive Tasks, Drag and Drop, Case Study, Multiple Choice, Lab
Exam Duration:100 minutes
Exam Price:$165 USD
Related Certifications:Microsoft Certified: Azure Data Scientist Associate
Sample Questions:Microsoft DP-100 Sample Questions
Exam Way:Online proctored exam or test center delivery through Pearson VUE.
Pre Condition:Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/

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一番いいDP-100試験勉強過去問 & 資格試験におけるリーダーオファー & 正確的なMicrosoft Designing and Implementing a Data Science Solution on Azure

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Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q334-Q339):

質問 # 334
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute! by using a terminal window from wofkspace1. You create a file named
"requirements.txt" containing Python dependencies to include Jupyler.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation


質問 # 335

You need to obtain the output from the pipeline execution. Where will you find the output?

正解：C

解説：
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and
'summary_only'.
* 'append_row' - All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
* 'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parall


質問 # 336
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation:

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector- machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature- importance


質問 # 337
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute! by using a terminal window from wofkspace1. You create a file named
"requirements.txt" containing Python dependencies to include Jupyler.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation:


質問 # 338
You need to define an evaluation strategy for the crowd sentiment models.
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.

正解：

解説：

1 - Define a cross-entropy function activation
2 - Add cost functions for each target state.
3 - Evaluated the distance error metric.
Reference:
https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/


質問 # 339
......

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