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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
Passing Score:700
Exam Price:$165 USD
Exam Format:Yes/No, Case studies, Multiple choice, Multiple select, Drag and drop
Available Languages:Arabic (Saudi Arabia), Russian, Italian, French, English, German, Korean, Chinese (Simplified), Spanish, Japanese, Portuguese (Brazil), Chinese (Traditional), Indonesian (Indonesia)
Real Exam Qty:40-60
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure AI Engineer Associate
Exam Duration:100 minutes
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: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

What is the duration of the DP-100 Exam

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

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

Microsoft DP-100: Exam Details

Microsoft doesn’t provide detailed information regarding its exams, including the DP-100 certification test. The total number of questions in exam is not published on the official website, but the candidates can expect to encounter about 40-60 items in their delivery of their test. There is also a high probability that they will face scenario-based questions with single-answer and multiple-answer options. The duration is all about 120 minutes. The exam is available in several languages, including Japanese, English, Simplified Chinese, and Korean.

The Microsoft DP-100 exam can be taken at any nearest Pearson VUE testing center or online as a proctored option. The cost for the exam is $165 in the United States but the price is usually different in other countries due to taxation. To find out the actual pricing for your country, simply check the official webpage.

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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

Question #1

Azure Machine Learning Studio を使用して実験を作成しています。
評価のためにデータを4つのサブセットに分割する必要があります。データには欠損値が多く含まれています。
分析用のデータを準備する必要があります。
実験を行うには適切な方法を選択する必要があります。
どの 3 つのモジュール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。
注意:正解の選択肢は複数あります。正解の選択肢の中からどれを選択しても、得点は加算されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.

Question #2

あなたはホテル予約ウェブサイト運営会社で働くデータサイエンティストです。Azure Machine Learning サービスを利用して、不正取引を識別するモデルをトレーニングします。
Azure Machine Learning SDK の Model.deploy メソッドを使用して、モデルを Azure Machine Learning リアルタイム Web サービスとしてデプロイする必要があります。デプロイされた Web サービスは、入力されたトランザクションデータに基づいて、不正行為のリアルタイム予測を返す必要があります。
モデルをデプロイするために使用される InferenceConfig クラスの entry_script パラメータとして指定されるスクリプトを作成する必要があります。
エントリ スクリプトでは何を行う必要がありますか?

  • A. 推論計算に必要なコア数とメモリ量を指定します。
  • B. Web サービスがデプロイされている推論クラスターでノードを起動します。
  • C. 適切なタグとプロパティを使用してモデルを登録します。
  • D. Web サービス コンピューティング用の Conda 環境を作成し、必要な Python パッケージをインストールします。
  • E. モデルをロードし、それを使用して入力データからラベルを予測します。
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

Explanation: Only visible for Easy4Engine members. You can sign-up / login (it's free).

Question #3

fsi という名前のファイル システムと、folder1 という名前のフォルダーを含む、storage1 という名前の Azure Data Lake Storage Gen2 保管アカウントを作成します。
folder1 の内容は、Azure Machine Learning ワークスペース内のコンピューティング ターゲット上のジョブからアクセスできる必要があります。
フォルダー 1 を参照するための URl を構築する必要があります。
URI はどのように構築すればよいですか? 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。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

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、指定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がありますが、他の質問セットには正しい解決策がない場合があります。
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。その結果、これらの質問はレビュー画面に表示されません。
履歴データに基づいて気象条件を予測するモデルを作成します。
データストアからデータを読み込み、処理されたデータを機械学習モデルのトレーニングスクリプトに渡すために、処理スクリプトを実行するパイプラインを作成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
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

Azure Machine Learning ワークスペースを管理します。
Azure Machine Learning Python SDK v2 を使用して、自然言語処理(NLP)テキスト分類モデルを複数の言語で実行する予定です。自動機械学習を使用して、テキスト分類ジョブの言語を構成する必要があります。TextClassifkationlob クラスのどのメソッドを使用すればよいですか?

  • A. データの設定
  • B. 特徴量化の設定
  • C. トレーニングパラメータの設定
  • D. set_sweep
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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