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 Exam Simulator
  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 20, 2026
  • Q & A: 528 Questions and Answers

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Microsoft DP-100日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Real Exam Qty:40-60
Exam Duration:100 minutes
Available Languages:Chinese (Traditional), Japanese, English, Korean, Portuguese (Brazil), Spanish, Indonesian (Indonesia), Italian, French, Chinese (Simplified), Arabic (Saudi Arabia), Russian, German
Related Certifications:Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure Data Engineer Associate
Exam Format:Yes/No, Multiple choice, Case studies, Drag and drop, Multiple select
Passing Score:700
Certificate Validity Period:1 year
Exam Price:$165 USD
Recommended Training:Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Microsoft Learn Learning Path
Exam Registration:Microsoft Learn Registration
Pearson VUE Scheduling
Sample Questions:Microsoft DP-100日本語 exam simulator
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

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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

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No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) Eligibility rules change from time to time, so confirm the current requirements on the official page (official DP-100日本語 exam page) before booking.

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The Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) is delivered Online proctored or onsite at Pearson VUE test centers — pick whichever arrangement fits you.

The Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) blueprint spans 4 domains — including Design and prepare a machine learning solution (20-25%), Train and deploy models (25-30%), Explore data and run experiments (20-25%). The weightings are your study map: allocate hours where the points are. Every subtopic appears in the outline above.

$165 USD per attempt, 700 to pass. Cost-effective preparation matters here: the 528 practice questions for the Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) cost a fraction of one retake.

Yes:

Training covers theory; our questions build test readiness. After any course, drill with the 528 practice questions for the Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) — the analyses make every wrong answer a lesson.

Yes — download the free DP-100日本語 demo before buying and assess the quality and reliability yourself; it's the best way to avoid wasting money on bootless material. Purchases include 365 days of free updates, renewable later at half price.

The Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) is Microsoft's certification exam for Microsoft Certified: Azure Data Scientist Associate, at the Associate level. Passing it demonstrates proficiency with specific technologies — a credential employers from small business to enterprise recognize. Related credentials include Microsoft Certified: Azure Data Engineer Associate, Microsoft Certified: Azure AI Engineer Associate.

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

Question #1

AmICompute クラスターとバッチ エンドポイントを含む Azure Machine Learning ワークスペースがあります。
MLflow モデルを含むリポジトリをローカルコンピューターにクローンします。モデルをバッチエンドポイントにデプロイできることを確認する必要があります。
解決策: ワークスペースにコンピューティング リソースを追加します。
ソリューションは目標を満たしていますか?

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

Correct Answer: A  🗳️

Question #2

Azure Machine Learning ワークスペースを作成し、MLflow ライブラリをインストールします。
MLflow ライブラリを使用して、さまざまな種類のデータを結合する必要があります。
どちらの方法を使用すればよいですか? 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。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Question #3

Workspace 1 Workspace! という名前の Azure Machine Learning ワークスペースがあり、これには PyFunc フレーバーを持つ model 1 という名前の登録済み Mlflow モデルがあります。Azure Machine Learning Python SDK vl を使用して、出力接続なしで、endpointl という名前のオンライン エンドポイントに model1 をデプロイする予定です。次のコードがあります。

モデルが正常にデプロイされるようにするには、ManagedOnllneDeployment オブジェクトにパラメーターを追加する必要があります。解決策: with_package パラメーターを追加します。
ソリューションは目標を満たしていますか?

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

Correct Answer: B  🗳️

Question #4

Azure Machine Learning Studio で線形回帰モデルを開発しています。さまざまなアルゴリズムを比較するための実験を実行します。
次の画像は結果データセットの出力を示しています。

ドロップダウン メニューを使用して、画像に表示されている情報に基づいて各質問に答える選択肢を選択します。
注意: 正しい選択ごとに 1 ポイントが付与されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

Question #5

Azure Machine Learning ワークスペースでモデルをトレーニングして登録します。
クライアントアプリケーションがバッチ推論にモデルを使用できるようにするパイプラインを公開する必要があります。入力データから予測を取得するには、Python推論スクリプトを実行する単一のParallelRunStepステップを含むパイプラインを使用する必要があります。
ParallelRunStep パイプライン ステップの推論スクリプトを作成する必要があります。
どの 2 つの関数を含め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。
注意: 正しい選択ごとに 1 ポイントが付与されます。

  • A. 実行(ミニバッチ)D
  • B. バッチ()
  • C. メイン()
  • D. スコア(ミニバッチ)
  • E. 初期化()
Reveal Solution  Discussion  0

Correct Answer: A,E  🗳️

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