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

DP-100日本語

Exam Code: DP-100J

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

Updated: Oct 17, 2026

Q & A: 528 Questions and Answers

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Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam

Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam which is related to Microsoft Certified Azure Data Scientist Associate Certification. This DP-100 exam validates the ability to apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. This DP-100 exam also tests the Candidate knowledge to use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates must have skills to use applications that involve natural language processing, speech, computer vision, and predictive analytics. Azure Data Scientist usually hold or pursue this certification and you can expect the same job role after completion of this certification.

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

Question #1
差分プライバシーを使用して、レポートが非公開であることを確認します。データのイプシロンの計算値は 1.8 です。レポートが非公開になるように、データを変更する必要があります。データに対してどのイプシロン値を受け入れる必要がありますか?

A. 0 ~ 1 の間
B. 2 ~ 3 の間
C. 3 ~ 10 の間
D. 10 以上


Question #2
Azure AI Foundry プロジェクトがあります。RAG ソリューションを開発します。
解決策を評価する必要があります。
評価に必要なデータを含む、適切な形式のファイルを提供する必要があります。解決策:.jsonl 形式のファイルを使用してください。
その解決策は目標を達成しているか?

A. いいえ
B. はい


Question #3
注:この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
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。その結果、これらの質問はレビュー画面に表示されません。
Azure Machine Learning Studioで新しい実験を作成しています。
1つのクラスには、トレーニングセットの他のクラスよりもはるかに少ない数の観測値があります。
クラスの不均衡を補うために、適切なデータサンプリング戦略を選択する必要があります。
解決策:サンプリングモードに層別分割を使用します。
ソリューションは目標を達成していますか?

A. いいえ
B. はい


Question #4
注: この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成す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解がある場合もあれば、正解がない場合もあります。
このセクションの質問に答えた後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Azure Machine Learning を使用して、分類モデルをトレーニングする実験を実行しています。
Hyperdrive を使用して、モデルの AUC メトリックを最適化するパラメーターを見つけたいと考えています。次のコードを実行して、実験用に HyperDriveConfig を構成します。

この構成を使用して、ランダム フォレスト モデルをトレーニングし、それを検証データでテストするスクリプトを実行する予定です。検証データのラベル値は y_test 変数という名前の変数に保存され、モデルからの予測確率は y_predicted という名前の変数に保存されます。
Hyperdrive が AUC メトリックのハイパーパラメータを最適化できるようにするには、スクリプトにログを追加する必要があります。解決策: 次のコードを実行します。

ソリューションは目標を達成していますか?

A. いいえ
B. はい


Question #5
C-サポートベクター分類を使用して、アンバランスなトレーニングデータセットで多クラス分類を実行します。Pythonコードを使用したC-サポートベクター分類を以下に示します。

C-Support Vector 分類コードを評価する必要があります。
どの評価ステートメントを使用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。


Solutions:

Question #1
Correct Answer: B
Question #2
Correct Answer: A
Question #3
Correct Answer: A
Question #4
Correct Answer: A
Question #5
Correct Answer: Only visible for members

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