Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版) : DP-600日本語 Exam Questions

Exam Code: DP-600J
Exam Name: Implementing Analytics Solutions Using Microsoft Fabric (DP-600日本語版)
Updated: Oct 15, 2026
Q&As: 212 Questions and Answers

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Microsoft DP-600日本語 Exam Syllabus Topics:

Topic	Details
Topic 1	Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.
Topic 2	Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.
Topic 3	Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.

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:

Question #1

Fabric のイベントハウス「Eventhousel」には、「Weatherdata」という名前のテーブルが含まれています。Weatherdata に含まれるデータのサンプルを次の表に示します。

あなたはKQLクエリセットを使用してEventhouse1のデータを操作する予定です。結果セットには以下の列が含まれている必要があります。

クエリを作成する必要があります。
どの4つのKQLステートメントを順番に使用すべきでしょうか？回答するには、ステートメントのリストから適切なステートメントを回答欄に移動させ、正しい順序に並べ替えてください。

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Comprehensive Detailed Explanation
Step 1: Understanding the requirement
We need a KQL query that:
Reads data from Weatherdata.
Orders by Datetime.
Calculates Delta_temperature as the difference between the current row and the preceding row.
Returns the final columns: Datetime, Lat, Long, Temperature, Delta_temperature.
Step 2: Evaluate the provided statements
Weatherdata # Starts the query with the source table.
| sort by Datetime asc # Ensures chronological order, required for prev() to work correctly.
| extend Delta_temperature = Temperature - prev(Temperature) # Computes difference between the current row and the previous row's temperature.
| project Datetime, Lat, Long, Temperature, Delta_temperature # Selects the required output columns.
Step 3: Incorrect choices (to avoid)
next(Temperature,1) # Would calculate the difference with the next row, not the previous.
prev(Temperature,2) # Would skip one row, giving the difference with two rows back, which is not needed.
summarize ... by Datetime # Aggregates the data; not required here since we just need row-level differences.
Step 4: Final Correct Sequence
Weatherdata
| sort by Datetime asc
| extend Delta_temperature = Temperature - prev(Temperature)
| project Datetime, Lat, Long, Temperature, Delta_temperature
References
prev() function in KQL
extend operator in KQL
project operator in KQL

Question #2

Lakehouse1 という名前のレイクハウスを含む Fabric テナントがあります。 Lakehouse1 には、CSV ファイルを含む Subfolder1 という名前のサブフォルダーが含まれています。 CSV ファイルを、V-Order 最適化が有効になっているデルタ形式に変換する必要があります。 Lakehouse エクスプローラーから何をすればよいですか?

A. テーブルへのロード機能を使用します。
B. 最適化機能を使用します。
C. [テーブル] セクションに新しいショートカットを作成します。
D. [ファイル] セクションに新しいショートカットを作成します。

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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

Question #3

Fabricテナントには、Modellという名前のセマンティックモデルが含まれています。Model!には、以下のテーブルが含まれています。

商品と店舗は複数のキャンペーンの一部となることができ、複数のアクティブなキャンペーンが同時に存在することも可能です。
あなたは、特定の店舗におけるキャンペーン後の売上を表示する「Report1」という名前のレポートを作成する予定です。
Modellにテーブルを追加し、テーブル間のリレーションシップのカーディナリティを設定する必要があります。何を設定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ポイントが加算されます。

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Question #4

あなたは湖畔の家を含むFabricテナントを所有しています。
あなたはFabricノートブックを使用して、以下のコードで大きなDataFrameを保存しています。

以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」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。

Reveal Solution Discussion 0

Correct Answer:

Explanation:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
DataFrame write partitionBy
Apache Spark optimization with partitioning