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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Oct 23, 2026
  • Q & A: 528 Questions and Answers

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

Microsoft DP-100日本語 Exam — Your Questions, Answered

The DP-100日本語 exam, officially titled Designing and Implementing a Data Science Solution on Azure (DP-100日本語版), is the Microsoft exam you pass to earn the Microsoft Certified: Azure Data Scientist Associate certification, a credential at the Associate level. Passing it confirms the skills defined in the official exam outline, and it is associated with related credentials such as Microsoft Certified: Azure Data Engineer Associate, Microsoft Certified: Azure AI Engineer Associate.

The DP-100日本語 exam includes 40-60 questions and gives you 100 minutes to finish them. That is a tight pace per question, so get used to reading each stem once, flagging anything uncertain, and moving on rather than getting stuck. Running timed practice tests in the DumpsActual desktop or online test engine is the most reliable way to build that rhythm before exam day.

The passing score for the DP-100日本語 exam is 700, and the official registration fee is $165 USD. Keep in mind that a failed attempt means paying $165 USD again in full to retake it, so book your slot only when your scores on DumpsActual practice tests sit consistently above the passing line.

Microsoft lists the following prerequisites or eligibility notes for the DP-100日本語 exam: No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow). Requirements can change over time, so confirm the details on the official exam page at Microsoft's official site before you register.

You can register for the DP-100日本語 exam through the official channels below:

The exam is delivered in the following format: Online proctored or onsite at Pearson VUE test centers. Choose a date that leaves you enough time to work through the full 528-question practice set first.

Microsoft recommends the following training resources for DP-100日本語 candidates:

Official courses build the theory, and the 528 practice questions from DumpsActual help you turn that theory into exam-ready speed and accuracy.

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The DP-100日本語 exam blueprint is divided into 4 domains, starting with Explore data and run experiments (20-25%); Train and deploy models (25-30%); Optimize language models for AI applications (25-30%). For the complete domain-by-domain breakdown, see the Exam Topics section above — it lists every topic the current outline covers.

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

Question #1

トレーニング クラスターと推論クラスターを含む Azure Machine Learning ワークスペースがあります。
Azure Machine Learning デザイナーを使用して分類モデルを作成する予定です。
クライアント アプリケーションが HTTP リクエストとしてデータを送信し、応答として予測を受信できることを確認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:

Question #2

Azure Machine Learning ワークスペースの所有者です。
カスタムロールを使用して、コンピューティングリソースの作成または削除を禁止する必要があります。ワークスペース内のその他のすべての操作を許可する必要があります。
カスタムロールを構成する必要があります。
設定をどのように完了すればよいですか? 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 ポイントが付与されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:


Box 1: Microsoft.MachineLearningServices/workspaces/*/read
Reader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can ' t create or update these assets.
Box 2: Microsoft.MachineLearningServices/workspaces/*/write
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/delete Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a- user-has-access-to-a-resource

Question #3

注: この質問は、同じシナリオを示す一連の質問の一部です。このシリーズの各質問には、指定された目標を達成できる可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策が含まれる場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答すると、その質問に戻ることはできません。そのため、これらの質問はレビュー画面には表示されません。
Azure Machine Learning ワークスペースがあります。Azure Machine Learning Studio の [ノートブック] ページからターミナル セッションに接続します。
同じターミナル セッションからアクセスできる新しい Jupyter カーネルを追加する予定です。
新しいカーネルを追加する前に、完了する必要があるタスクを実行する必要があります。
解決策: Python 3.8 - AzureML カーネルを削除します。
解決策は目標を達成できますか?

  • A. はい
  • B. いいえ
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

Azure Machine Learning モデルを作成し、モデルファイルとスクリプトを組み込みます。このモデルをデプロイする必要があります。デプロイソリューションは、以下の要件を満たす必要があります。
* ほぼリアルタイムの推論を提供します。
* エンドポイントおよび展開レベルのコスト見積りを有効にします。
* Azure Log Analytics へのログ記録をサポートします。
デプロイメント ソリューションを構成する必要があります。
何を設定する必要がありますか? 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 ポイントが付与されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Question #5

Azure Machine Learning ワークスペースを管理します。
Mlftow でログを記録するカスタムモデルを構築します。カスタムモデルには以下のものが含まれます。
* このモデルは、Mlflow ではネイティブにサポートされていません。
* モデルは Pickle 形式でシリアル化できません。
* モデルのソースコードは複雑です。
* モデルの Python ライブラリはモデルと一緒にパッケージ化する必要があります。
ML フローによるログ記録を有効にするには、カスタム モデル フレーバーを作成する必要があります。
何を使うべきでしょうか?

  • A. カスタム署名
  • B. モデルラッパー
  • C. モデルローダー
  • D. アーティファクト
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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