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

DP-100日本語 Exam Questions
  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Oct 11, 2026
  • Q & A: 528 Questions and Answers
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What is the duration of the DP-100 Exam

  • Length of Examination: 150 minutes
  • Format: Multiple choices, multiple answers
  • Passing Score: 700/1000
  • Number of Questions: 45-60

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  • Organization owners invest a lot in their employees when it comes to their training with the goal of making them quicker, more efficient, and more knowledgeable about their role. Certified Professional will reduce the time he spends on tasks, meaning he can get more done this could help reduce company downtime when repairing faults on a system or fixing hardware problems.
  • When Candidates applying for a job or looking to promotion in their current position, a Microsoft Certified Azure Data Scientist Associate certification in the field in which Candidates are applying will put you at the top of the list and make them a desirable candidate for employers.
  • Candidates will get in-depth knowledge by completing the courses along with the access to revision materials for 6 months upon completion means they will have a wider skill set when it comes to the various technologies and systems than an uncertified professional. Certified Professional in this particular skill set is 74% more efficient when it comes to completing their tasks in a timely well-executed manner.
  • After completion of Microsoft Certified Azure Data Scientist Associate Certification candidates receive official confirmation from Microsoft that you are now fully certified in their chosen field. This can be now added to their CV, cover letters and job applications.
  • Becoming Microsoft Certified Azure Data Scientist Associate means one thing you are worth more to the company and therefore more to yourself in the form of an upgraded pay package. On average a Microsoft Certified Azure Data Scientist Associate member of staff is estimated to be worth 30% more to a company than their uncertified professionals.

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

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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

Question #1

Azure Machine Learning ワークスペースを管理します。
パイプライン コンポーネントの出力として urijolder データ型を選択します。
構成でサポートされているデータ アクセス モードを定義する必要があります。
どのモードを定義する必要がありますか?

  • A. 直接
  • B. rw_mount
  • C. ダウンロード
  • D. eva|-ダウンロード
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

Workspace1 という名前の Azure Machine Learning ワークスペースを管理します。
アクセスキーを使用して、ワークスペース1 に Azure Blob Storage データストアを登録する必要があります。データストアの登録に必要なすべてのモジュールをインポートするための Python SDK v2 コードを開発します。
データストアを定義するには、Python SDK v2 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

財務チームから、finance-data という名前の Azure Storage BLOB コンテナー内のデータを使用してモデルをトレーニングするように依頼されました。
コンテナーを Azure Machine Learning ワークスペースのデータストアとして登録し、コンテナーが存在しない場合にエラーが発生するようにする必要があります。
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:

Box 1: register_azure_blob_container
Register an Azure Blob Container to the datastore.
Box 2: create_if_not_exists = False
Create the file share if it does not exists, defaults to False.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore

Question #4

機械学習モデルを使用してインテリジェントなソリューションを構築しています。
環境は次の要件をサポートする必要があります。
データサイエンティストはクラウド環境でノートブックを構築する必要がある
データ サイエンティストは、機械学習パイプラインで自動特徴エンジニアリングとモデル構築を使用する必要があります。
動的なワーカー割り当てを備えた Spark インスタンスを使用して再トレーニングするには、ノートブックをデプロイする必要があります。
ノートブックは、ローカルでバージョン管理するためにエクスポート可能である必要があります。
環境を整える必要があります。
どの 4 つのアクションを順番に実行する必要がありますか? 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: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.
Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook
https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

Question #5

Azure Machine Learning ワークスペースがあり、スイープジョブを使用してモデルのハイパーパラメータを調整する予定です。
低パフォーマンスのジョブの早期終了と継続的なハイパーパラメータをサポートするサンプリング方法を見つける必要があります。
解決策: ハイパーパラメータ空間で Sobol サンプリング法を使用します。
ソリューションは目標を満たしていますか?

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

Correct Answer: A  🗳️

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