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

Microsoft DP-100日本語 Actual PDF
  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 19, 2026
  • Q & A: 528 Questions and Answers
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About Microsoft DP-100日本語 Exam

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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
Available Languages:Italian, Indonesian (Indonesia), Spanish, Korean, Russian, French, Portuguese (Brazil), English, Japanese, Chinese (Simplified), German, Chinese (Traditional), Arabic (Saudi Arabia)
Exam Format:Multiple choice, Drag and drop, Multiple select, Yes/No, Case studies
Exam Price:$165 USD
Real Exam Qty:40-60
Passing Score:700
Exam Duration:100 minutes
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure AI Engineer Associate
Certificate Validity Period:1 year
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:Free Download DP-100日本語 Test PDF
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. Use Azure AI Foundry
  • 2. Build prompt flows
  • 3. Apply prompt engineering
- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Prepare and process data
  • 3. Create vector stores and indexes
- Evaluate and improve models
  • 1. Test and evaluate responses
  • 2. Optimize for accuracy and safety
  • 3. Apply responsible generative AI
Topic 2: Train and deploy models25-30%- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Update and retrain models
  • 3. Monitor performance and data drift
- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Apply responsible AI principles
  • 3. Configure jobs and environments
  • 4. Run training scripts
- 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. Configure compute and scaling
  • 3. Deploy to online endpoints
  • 4. Secure endpoints and manage access
Topic 3: Design and prepare a machine learning solution20-25%- Design a machine learning solution
  • 1. Select development approach
  • 2. Define compute specifications for workloads
  • 3. Determine dataset structure and format
  • 4. Plan model deployment requirements
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Select environments
  • 3. Create and configure compute targets
- 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. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
Topic 4: Explore data and run experiments20-25%- Explore and visualize data
  • 1. Detect anomalies and outliers
  • 2. Identify features and relationships
  • 3. Profile and validate data
- Run experiments
  • 1. Use automated machine learning
  • 2. Configure experiment runs
  • 3. Define parameters and configurations
  • 4. Track runs with MLflow
- Implement pipelines
  • 1. Schedule and monitor pipelines
  • 2. Build reusable components
  • 3. Pass data between steps
  • 4. Create and publish pipelines

Clear Answers for Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) Candidates

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The Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) blueprint covers these principal domains:

  • Train and deploy models (25-30%)
  • Explore data and run experiments (20-25%)
  • Design and prepare a machine learning solution (20-25%)

The remaining domains appear in the full official outline, all of which our bank addresses.

As of the latest information, the passing score for the DP-100日本語 exam is 700 and the fee is $165 USD. Microsoft can revise both, so confirm the current figures on the official site before registering.

Registration runs through the official channels below:

Choose your center or online session, and reserve early for the most convenient dates.

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Microsoft recommends these official training options:

Pick the course matching your experience, then reinforce it with regular question practice.

Microsoft lists the following prerequisites for the Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版): No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow).

Confirm the details on the official certification page before you book.

Per current exam information, the DP-100日本語 exam includes 40-60 questions and allows 100 minutes minutes. Timed practice beforehand makes the format feel routine on the day.

Because every step respects your time. Buying is a simple, transparent procedure: choose your version or package, see the cost generated automatically, confirm, and order — then the materials arrive by email in about a minute. The DP-100日本語 content is clear, the main points easy to acquire, and every answer expert-verified; the PDF prints for paper review. When the exam changes, our experts devote their energy to immediate research and revision, and critical comments trigger improvement measures as soon as possible. A free demo lets you run a mini-test and confirm quality first, and your personal information is protected on an integrity-based platform throughout.

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

Question #1

ローカルペナルティ検出データのスケーリング戦略を実装する必要があります。
どの正規化タイプを使用する必要がありますか?

  • A. Weight
  • B. Cosine
  • C. Batch
  • D. Streaming
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

ディープ ラーニング仮想マシン (DLVM) を使用して、Compute Unified Device Architecture (CUDA) 計算を使用したディープ ラーニング モデルをトレーニングする予定です。
CUDA をサポートするには DLVM を構成する必要があります。
何を実装する必要がありますか?

  • A. オーバークロックによるコンピュータ処理装置(CPU)の速度向上
  • B. グラフィック プロセッシング ユニット (GPU)
  • C. ソリッドステートドライブ (SSD)
  • D. 高ランダムアクセスメモリ(RAM)構成
  • E. インテル ソフトウェア ガード エクステンション (インテル SGX) テクノロジー
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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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を持ちます。
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:主成分分析(PCA)サンプリングモードを使用します。
ソリューションは目標を達成していますか?

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

Correct Answer: B  🗳️

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Question #4

Azure At Foundry プロジェクトでチャット アプリケーションを開発しています。
GPT-4o ベースモデルを選択します。サンプルプロンプトとレスポンスを含む一連のトレーニングデータを使用して、モデルを微調整する予定です。微調整ジョブに使用するマルチターンチャットファイルを JSONL 形式で作成する必要があります。
JSONLファイルのメッセージコンポーネントで定義すべき3つの変数はどれですか?正解はそれぞれ解答の一部です。3つ選択してください。注:正解1つにつき1点が加算されます。

  • A. システム
  • B. 重量
  • C. 役割
  • D. コンテンツ
  • E. ユーザー
Reveal Solution  Discussion  0

Correct Answer: A,C,D  🗳️

Question #5

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

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