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Microsoft DP-100日本語 Exam : Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)

DP-100日本語 actual test
  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Nov 07, 2026
  • Q & A: 528 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $69.99  

About Microsoft DP-100日本語 Exam

Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

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DP-100 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our DP-100 exam dumps will include the following topics:

  • Prepare data for modeling 25-30%
  • Develop models 40-45%
  • Define and prepare the development environment 15-20%
  • Perform feature engineering 15-20%

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Career Opportunities & Salary Outlook

The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.

Microsoft DP-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explore data and run experiments20-25%- Implement pipelines
  • 1. Build reusable components
  • 2. Schedule and monitor pipelines
  • 3. Create and publish pipelines
  • 4. Pass data between steps
- Run experiments
  • 1. Configure experiment runs
  • 2. Track runs with MLflow
  • 3. Define parameters and configurations
  • 4. Use automated machine learning
- Explore and visualize data
  • 1. Identify features and relationships
  • 2. Profile and validate data
  • 3. Detect anomalies and outliers
Topic 2: Train and deploy models25-30%- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
- Deploy models
  • 1. Deploy to online endpoints
  • 2. Secure endpoints and manage access
  • 3. Configure compute and scaling
  • 4. Deploy to batch endpoints
- Train models
  • 1. Run training scripts
  • 2. Configure jobs and environments
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Apply responsible AI principles
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Implement MLOps practices
  • 3. Update and retrain models
Topic 3: Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 3. Register and manage datastores
- Manage compute resources
  • 1. Select environments
  • 2. Attach and monitor compute
  • 3. Create and configure compute targets
- Manage Azure Machine Learning workspace
  • 1. Use developer tools and CLI
  • 2. Create and configure workspace
  • 3. Set up Git integration
  • 4. Work with registries
- Design a machine learning solution
  • 1. Plan model deployment requirements
  • 2. Determine dataset structure and format
  • 3. Select development approach
  • 4. Define compute specifications for workloads
Topic 4: Optimize language models for AI applications25-30%- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Prepare and process data
  • 3. Create vector stores and indexes
- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Apply prompt engineering
  • 3. Use Azure AI Foundry
- Evaluate and improve models
  • 1. Optimize for accuracy and safety
  • 2. Apply responsible generative AI
  • 3. Test and evaluate responses

Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) Sample Questions:

Question #1

Azure Machine Learning Studioの2クラスニューラルネットワークモジュールを使用して、バイナリ分類モデルを構築します。 Tune Model Hyperparametersモジュールを使用して、モデルの精度を調整します。
Tune Model Hyperparametersモジュールを使用して、調整する必要があるハイパーパラメーターを選択する必要があります。
どの2つのハイパーパラメーターを使用する必要がありますか?それぞれの正解はソリューションの一部を示しています。
注:それぞれの正しい選択には1ポイントの価値があります。

  • A. The type of the normalizer
  • B. Number of learning iterations
  • C. Number of hidden nodes
  • D. Learning Rate
  • E. Hidden layer specification
Reveal Solution  Discussion  0

Correct Answer: B,E  🗳️

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Question #2

バイナリ分類を実行するためにリカレント ニューラル ネットワークを構築しています。
各トレーニングエポックのトレーニング損失、検証損失、トレーニング精度、検証精度が提供されています。分類モデルが過剰適合していないかどうかを確認する必要があります。
次のどれが正しいでしょうか?

  • A. モデルのトレーニング時に、トレーニング損失は一定のままで、検証損失は一定値のままで、トレーニング損失値に近くなります。
  • B. モデルをトレーニングすると、トレーニング損失は一定のままで、検証損失は減少します。
  • C. モデルのトレーニング時に、トレーニング損失は減少し、検証損失は増加します。
  • D. モデルのトレーニング時に、トレーニング損失は増加し、検証損失は減少します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

複数の生徒に実践的なワークショップを実施する予定です。ワークショップでは、Pythonを使用したデータ視覚化の作成に焦点を当てます。各生徒は、インターネットにアクセスできるデバイスを使用します。
学生用デバイスはPython開発用に構成されていません。学生には、デバイスにソフトウェアをインストールするための管理者アクセス権がありません。学生はAzureサブスクリプションを利用できません。
学生がPythonベースのデータ視覚化コードを実行できることを確認する必要があります。
どのAzureツールを使用する必要がありますか?

  • A. Azure BatchAl
  • B. Azure Machine Learning Service
  • C. Azure Notebooks
  • D. Anaconda Data Science Platform
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

Azure Machine Learning Studio で多重線形回帰モデルを作成しています。
いくつかの独立変数は高度に相関しています。
すべてのデータに対して効果的な特徴エンジニアリングを実行するには、適切な方法を選択する必要があります。
順番に実行する必要がある 3 つのアクションはどれですか。回答するには、適切なアクションをアクション リストから回答領域に移動し、正しい順序に並べます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature- selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

Question #5

Azure Machine Learning ワークスペースがあります。
Azure Machine Learning Python SDK v2 を使用してワークスペースにコンポーネントを登録する予定です。コンポーネント定義は、ローカル ファイル ./components/train/train.yml に保存されます。
ml_client オブジェクトを使用してワークスペースに接続し、必要なすべてのライブラリをインポートするコードを記述します。残りのコード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、回答領域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 ポイントが加算されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

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