Microsoft DP-700日本語 : Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700日本語版)

  • Exam Code: DP-700日本語
  • Exam Name: Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700日本語版)
  • Updated: Oct 20, 2026     Q & A: 144 Questions and Answers

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Microsoft DP-700日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Monitor and optimize an analytics solution: This section of the exam measures the skills of Data Analysts in monitoring various components of analytics solutions in Microsoft Fabric. It focuses on tracking data ingestion, transformation processes, and semantic model refreshes while configuring alerts for error resolution. One skill to be measured is identifying performance bottlenecks in analytics workflows.
Topic 2
  • Ingest and transform data: This section of the exam measures the skills of Data Engineers that cover designing and implementing data loading patterns. It emphasizes preparing data for loading into dimensional models, handling batch and streaming data ingestion, and transforming data using various methods. A skill to be measured is applying appropriate transformation techniques to ensure data quality.
Topic 3
  • Implement and manage an analytics solution: This section of the exam measures the skills of Microsoft Data Analysts regarding configuring various workspace settings in Microsoft Fabric. It focuses on setting up Microsoft Fabric workspaces, including Spark and domain workspace configurations, as well as implementing lifecycle management and version control. One skill to be measured is creating deployment pipelines for analytics solutions.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-700/

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700日本語版) Sample Questions:

Question #1

ドラッグ&ドロップ
KQLデータベースを含むFabricイベントハウスがあります。データベースにはTaxiDat aというテーブルが含まれています。以下はTaxiDataのデータのサンプルです。

2つのKQLクエリを構築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。
クエリの 1 つは、RunningTotalAmount を VendorID でパーティション分割する必要があります。
もう 1 つのクエリでは、payment_type でパーティション化された tpep_pickup_datetime から各時間の最初の値を表示する FirstPickupDateTime という列を作成する必要があります。
各クエリをどのように完了すればよいですか?回答するには、適切な値を正しいターゲットにドラッグしてください。各値は1回、複数回、または全く使用されない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
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:

Partition the RunningTotalAmount by VendorID. - Row_cumsum
The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).

Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type - Row_window_session

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。
StreamとReferenceという2つのテーブルを含むKQLデータベースがあります。Streamには、次の形式のストリーミングデータが含まれています。

参照には次の形式の参照データが含まれます。

どちらのテーブルにも数百万の行が含まれています。
次の KQL クエリセットがあります。

KQL クエリセットの実行にかかる時間を短縮する必要があります。
解決策: フィルターを行 02 に移動します。
これ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: B  🗳️

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Question #3

FabricワークスペースにWorkspace1という名前があり、その中にLakehouse1という名前で湖畔の家があります。
PySparkベースのデータ変換プロセスを自動化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。
* ユーザーがPySparkコードを対話的に編集およびテストできるようにする。
* コードが自動実行に対応できることを確認してください。
どの3つの行動を順番に行うべきですか?

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
The correct sequence is Create a notebook # Attach the notebook to Lakehouse1 # Create a data pipeline
.
First, create a Fabric notebook because notebooks provide the interactive development environment required for authoring, executing, debugging, and testing PySpark code. Microsoft describes Fabric notebooks as a primary code item for developing Apache Spark workloads and specifically supports their use for data ingestion, preparation, and transformation.
Second, attach the notebook to Lakehouse1 . Attaching a lakehouse establishes the notebook ' s lakehouse context, allowing PySpark code to access the lakehouse ' s Files and Tables through Spark and OneLake. This enables developers to interactively validate the transformation against the required Fabric data.
Third, create a data pipeline and use a Notebook activity to operationalize the notebook. Fabric Data Factory explicitly supports running Fabric notebooks as pipeline activities for automated data transformation workflows. Pipelines can subsequently be run manually, scheduled, or incorporated into larger orchestration processes.
Converting the notebook to an Apache Spark job definition is unnecessary; Spark job definitions are separate executable Fabric items. Publishing the notebook is also not required for pipeline execution.
Study Guide reference: Ingest and transform data # Transform data with Apache Spark # Fabric notebooks # Orchestrate processes and schedules with Data Factory pipelines.

Question #4

ブロンズ レイヤーとシルバー レイヤーのプロセスが分離して実行されるようにする必要があります。Apache Spark 設定をどのように構成すればよいですか?

  • A. デフォルトの環境を設定します。
  • B. 実行者の数を変更します。
  • C. カスタム プールを作成します。
  • D. 高い同時実行性を無効にします。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

DW1 という名前のウェアハウスを含む Fabric ワークスペースがあります。DW1 は、Notebook1 という名前のノートブックを使用して読み込まれます。
Notebook1 の実行時に使用された Delta のバージョンを特定する必要があります。
何を使うべきでしょうか?

  • A. ファブリックモニター
  • B. Microsoft Fabric Capacity Metrics アプリ
  • C. 管理者監視ワークスペース
  • D. OneLakeデータハブ
  • E. リアルタイムハブ
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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