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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Passing Score: | 700/1000 |
| Exam Price: | $165 USD |
| Certificate Validity Period: | 1 year |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40-60 |
| Available Languages: | English, Chinese (Simplified), Korean, French, German, Spanish, Portuguese, Japanese |
| Exam Format: | Multiple Choice, Lab, Case Study, Interactive Tasks, Drag and Drop |
| 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/ |
| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Explore data and train models | 35-40% | - Optimize model performance
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
|
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The latest exam information lists 40-60 questions for the DP-100日本語 exam within a 100 minutes-minute window. Practicing under those exact conditions is the best cure for exam-day surprises.
The Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) blueprint is organized around these core domains:
Further domains appear in the full official outline; the question bank covers all of them.
By fitting study into the cracks of a busy life. The DP-100日本語 bank contains complete questions with expert-verified answers across the Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) objectives, stored easily on your phone, pad, or computer for spare-time review — and the PDF prints cleanly when you prefer paper, because marking key points by hand genuinely enhances memory. Assess everything first through the free demo on our site, pay securely through the Credit Card system, and rest assured your personal information is never shared with third parties without permission.
Microsoft specifies these prerequisites for the Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版): Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow..
Check the authoritative wording on the official certification page before booking.
At present, the DP-100日本語 exam requires a passing score of 700/1000, and registration costs $165 USD. Microsoft sets both figures and may adjust them, so verify on the official site before you schedule.
Azure ML SDKを使用してバッチ推論パイプラインを作成します。パイプラインを実行するには、次のコードを使用します。
azureml.pipeline.core からパイプラインをインポートします
azureml.core.experiment から Experiment をインポートします
パイプライン = パイプライン(ワークスペース = ws、ステップ = [parallelrun_step])
pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)
パイプライン実行の進行状況を監視する必要があります。
この目標を達成するには、どのような 2 つの方法がありますか? それぞれの正解は完全な解決策を示します。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer: C,D 🗳️
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Azure Machine Learning ワークスペースを使用します。
エンドポイントと展開レベルでコストを監視する必要があります。
トレーニング済みのモデルをオンラインエンドポイントとしてデプロイする必要があります。ユーザーはMicrosoft Entra IDを使用して認証する必要があります。
何をすべきでしょうか?
Correct Answer: C 🗳️
Azure Machine Learningワークスペースを管理する
カスタムコンポーネントを使用して、画像分類用のAzure Machine Learningパイプラインを構築します。Python関数からコンポーネントを実行するためのインターフェイス、メタデータ、コードを定義する必要があります。どの関数を使用すればよいでしょうか？
Correct Answer: C 🗳️
Azure Machine Learning のリモート コンピューティングでトレーニング実験を実行しています。
実験は、mlflow および azureml-contrib-run パッケージを含む conda 環境を使用するように構成されています。
実験で生成されたメトリックを追跡するためのログ パッケージとして MLflow を使用する必要があります。
実験のスクリプトを完成させる必要があります。
コードをどのように完成させるべきですか? 回答するには、回答エリアで適切なオプションを選択してください。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer:
Explanation:
Box 1: import mlflow
Import the mlflow and Workspace classes to access MLflow ' s tracking URI and configure your workspace.
Box 2: mlflow.start_run()
Set the MLflow experiment name with set_experiment() and start your training run with start_run().
Box 3: mlflow.log_metric( ' .. ' )
Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
Box 4: mlflow.end_run()
Close the run:
run.endRun()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。注：正しい選択肢は1つにつき1点です。
Correct Answer:
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