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Updated: Oct 22, 2026

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

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.

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

For more info visit:

Microsoft DP-100 Exam Reference

The dream of becoming a highly skilled data scientist can turn into a reality with the help of the Microsoft DP-100 exam. This exam tries to impart an associate-level understanding of data science and machine learning with an aim to generate a skilled workforce of data scientists.

What Next after This Certification?

The certification obtained from passing DP-100 exam is proof of intermediate skills in data science since it is within the associate hierarchy. Apart from applying for and getting a job in a preferred company, you can also expand your skills in an area of interest. With the role-based scheme, there are expert certifications awaiting you to earn. They include the Microsoft Certified: Azure Solutions Architect Expert and the Microsoft Certified: Azure DevOps Engineer Expert.

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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

Question #1

注: この問題は、同じシナリオを提示する一連の問題の一部です。一連の問題にはそれぞれ、定められた目標を満たす可能性のある独自の解答が含まれています。問題セットによっては、複数の正解が存在する場合もあれば、正解がない場合もあります。
このセクションの質問に回答した後は、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表示されません。
Azure Machine Learning デザイナーを使用して、次のデータセットを実験に読み込みます。

入力データセットと同じ列とヘッダー行を持ち、両方の入力データセットのすべての行が含まれるデータセットを作成する必要があります。
解決策: 行の追加モジュールを使用します。
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 #2

注: この問題は、同じシナリオを提示する一連の問題の一部です。一連の問題にはそれぞれ、定められた目標を満たす可能性のある独自の解答が含まれています。問題セットによっては、複数の正解が存在する場合もあれば、正解がない場合もあります。
このセクションの質問に回答した後は、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を見つけます。次のコードを実行して、実験用の HyperDriveConfig を設定します。

y_testという変数に格納され、モデルから予測された確率はy_predictedという変数に格納されます。HyperdriveがAUC指標のハイパーパラメータを最適化できるようにするには、スクリプトにログ記録を追加する必要があります。解決策:次のコードを実行してください。

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  🗳️

Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

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。
複数の列に欠損値を含む数値データセットを分析しています。
機能セットの次元に影響を与えることなく、適切な操作を使用して欠損値を消去する必要があります。
すべての値を含めるには、完全なデータセットを分析する必要があります。
解決策:欠落しているデータポイントを含む列全体を削除します。
ソリューションは目標を達成していますか?

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

Correct Answer: B  🗳️

Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

Question #4

Azure Machine Learning Designerを使用して、トレーニングパイプラインを作成して実行します。次に、リアルタイムの推論パイプラインを作成します。
リアルタイム推論パイプラインをWebサービスとしてデプロイする必要があります。
リアルタイム推論パイプラインをデプロイする前に、何をする必要がありますか?

  • A. バッチ推論パイプラインを作成します。
  • B. Azure MachineLearningコンピューティングクラスターを作成します。
  • C. トレーニングパイプラインのクローンを作成します。
  • D. リアルタイム推論パイプラインを実行します。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).

Question #5

外れ値を削除する前に、年齢列に外れ値が存在するかどうかを視覚的に識別し、外れ値を定量化する必要があります。
どの 3 つの Azure Machine Learning Studio モジュールを順番に使用する必要がありますか? 回答するには、適切なモジュールをモジュール 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:
Create Scatterplot
Summarize Data
Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

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