Microsoft DP-600日本語 : Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版)
- Exam Code: DP-600日本語
- Exam Name: Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版)
- Updated: Oct 22, 2026
- Q & A: 212 Questions and Answers
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600
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Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版) Sample Questions:
「Model 1」という名前のセマンティックモデルがあります。Model 1には、すべてインポートモードを使用する5つのテーブルが含まれています。Model1には、「HR」という名前の動的な行レベルセキュリティ(RLS)ロールが含まれています。HRロールは従業員データをフィルタリングし、人事マネージャーが割り当てられた部門のデータのみを表示できるようにします。
Model1をFabricテナントに公開し、RLSロールメンバーシップを設定します。モデルと関連レポートをユーザーと共有します。
人事部長は、報告書に記載されているデータが不完全であると報告している。
人事部長が閲覧したデータの妥当性を検証するために、あなたは何をすべきですか？
- A. レポート内のデータを、人事部のフィルターの意図したロジックに一致するようにフィルタリングします。
- B. 人事部長に、Microsoft Power BI Desktop でレポートを開くように依頼してください。
- C. 人事担当者としてデータを表示するには、役割として「テスト」を選択してください。
- D. 役割として「テスト」を選択すると、人事マネージャーとしてレポートが表示されます。
Correct Answer: D 🗳️
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Fabricテナントには、Workspace1とWorkspace2という名前の2つのワークスペースと、User1という名前のユーザーが存在します。
User1が以下のタスクを実行できることを確認する必要があります。
新しいドメインを作成します。
subdomain1とsubdomain2という名前の2つのサブドメインを作成します。
Workspace1をsubdomain1に割り当てます。
Workspace2をsubdomain2に割り当てます。
解決策は最小権限の原則に従わなければならない。
User1にはどの役割を割り当てるべきですか？
- A. ワークスペース管理者
- B. ファブリック管理者
- C. ドメイン管理者
- D. ドメイン貢献者
Correct Answer: C 🗳️
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顧客満足度レポートのためのセマンティックモデルを設計する必要があります。
どのデータソース認証方法とモードを使用すべきですか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.
Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS.
Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user ' s credentials.
Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.
Based on these considerations, the selections should be:
Authentication method: Service principal authentication
Mode: DirectQuery
以下のT-SQIステートメントがあります。
Correct Answer:
Explanation:
The statement uses SUM(CASE WHEN RefundStatus = ' Refunded ' THEN SalesAmount ELSE 0 END) AS TotalRevenue, which calculates TotalRevenue by summing SalesAmount only when RefundStatus is ' Refunded ' , and 0 otherwise. This means Region values are returned regardless of RefundStatus, but the TotalRevenue reflects refunded items.
The WHERE (TransactionDate) - YEAR(GETDATE()) condition is incomplete and lacks proper comparison (e.g., no equality or range check), so it does not filter for the current year.
The TotalRevenue calculation does not aggregate all SalesAmount values with RefundStatus of ' Refunded ' ; it sums SalesAmount only for refunded items within the grouped data, with 0 for non-refunded items.
Fabricテナントには、Lakehouse1という名前の湖畔の家が含まれています。Lakehouse1には、Nyctaxi_rawという名前のテーブルが含まれています。Nyctaxi_rawには、次の列が含まれています。
Fabricノートブックを作成し、lakehouse1に添付します。
データの変換にはPySparkコードを使用する必要があります。ソリューションは以下の要件を満たす必要があります。
* pickupDateTime の日付部分のみを含む pickupDate という名前の列を追加します。
* データフレームをフィルタリングして、fareAmountが100未満の正の数である行のみを含めるようにします。
コードを完成させるにはどうすればよいでしょうか？回答するには、回答欄で適切なオプションを選択してください。注：
正解ごとに1ポイント獲得できます。
Correct Answer:
Explanation:
Add the pickupDate column: .withColumn( " pickupDate " , df[ " pickupDateTime " ].cast( " date " )) Filter the DataFrame: .filter( " fareAmount > 0 AND fareAmount < 100 " ) In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast( " date " ) function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than
0 and less than 100, thus ensuring only positive values less than 100 are included.
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