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多くの人は結果が大丈夫で過程だけ重要ですって言いますが。MicrosoftのDP-100試験にとってはそうではない。MicrosoftのDP-100試験に合格するのはIT業界で働いているあなたに利益をもらわせることができます。もしあなたが試験に合格する決心があったら、我々のMicrosoftのDP-100ソフトを利用するのはあなたの試験に成功する有効な保障です。我々のMicrosoftのDP-100ソフトのデモをダウンロードしてみて我々CertJukenのあなたに合格させる自信を感じられます。
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 |
| Exam Price: | $165 USD |
| Available Languages: | Japanese, French, Chinese (Simplified), English, Spanish, Portuguese (Brazil), Chinese (Traditional), Indonesian (Indonesia), Korean, Arabic (Saudi Arabia), German, Italian, Russian |
| Certificate Validity Period: | 1 year |
| Exam Duration: | 100 minutes |
| Exam Format: | Drag and drop, Yes/No, Multiple choice, Case studies, Multiple select |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate |
| Recommended Training: | Microsoft Learn Learning Path Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Exam Registration: | Microsoft Learn Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 |
DP-100試験ガイドには、高品質のサービスがあります。 24時間のオンラインサービスを提供しています。 DP-100試験問題の使用中に質問がある場合は、メールでお問い合わせください。 私たちMicrosoftは最大限の忍耐と態度で優れたアフターサービスを提供します。 そして、DP-100練習用トレントの使用中に発生する問題に対する詳細な解決策を提供します。 そして、DP-100の学習資料は、あなたの監督と批判を歓迎します。 私たちのDP-100学習教材の会社で、あなたはDesigning and Implementing a Data Science Solution on Azure成功の方向を見つけるでしょう。
Microsoft DP-100認定試験は、Azureサービスを使用したデータサイエンスソリューションの設計と実装の知識とスキルをテストするために設計されています。この認定試験は、データサイエンティスト、データエンジニア、および他の関係者と協力して、機械学習（ML）および人工知能（AI）ソリューションを開発および展開する個人を対象としています。
質問 # 96 
You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?
正解：A
解説：
Log a numerical or string value to the run with the given name using log(name, value, description='').
Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run
質問 # 97
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
正解：A
解説：
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear- discriminant-analysis https://docs.microsoft.com/en-us/azure/machine-learning/studio-module- reference/compute-linear-correlation
質問 # 98
Drag and Drop Question
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal.
You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning Python SDK v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties, with frequency set to "month" and interval set to "1".
Which three classes should you instantiate 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：
Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipeline- job?view=azureml-api-2&tabs=python#schedule-a-pipeline-job
質問 # 99
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
正解：C
解説：
Use the SMOTE module in Azure Machine Learning Studio (classic) 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.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
質問 # 100
The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.
You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: register_azure_blob_container
Register an Azure Blob Container to the datastore.
Box 2: create_if_not_exists = False
Create the file share if it does not exists, defaults to False.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore
質問 # 101
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DP-100学習関連題: https://www.certjuken.com/DP-100-exam.html
ちなみに、CertJuken DP-100の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=13Jes_3saikw5lXDJaTLnS4Q5QtwIYimd