2025年最新の有効なDP-600試験最新問題で2025年最新の学習ガイド [Q12-Q28]

2025年最新の有効なDP-600試験最新問題で2025年最新の学習ガイド

DP-600認定で究極のガイド [2025年更新]

Microsoft DP-600 認定試験の出題範囲：

トピック	出題範囲
トピック 1	データ分析のソリューションを計画、実装、管理する: このトピックでは、データ分析環境の計画、データ分析環境の実装と管理について説明します。また、分析開発ライフサイクルの管理にも重点を置いています。
トピック 2	データの準備と提供: このトピックでは、レイクハウスまたはウェアハウスでのオブジェクトの作成、データのコピー、データの変換、パフォーマンスの最適化に関する質問が表示されます。
トピック 3	セマンティックモデルの実装と管理: このトピックでは、セマンティックモデルの設計と構築、およびエンタープライズ規模のセマンティックモデルの最適化について詳しく説明します。
トピック 4	データの探索と分析: 探索的分析の実行についても説明します。さらに、このトピックでは、SQL を使用してデータをクエリする方法について詳しく説明します。

質問 # 12
You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model. What should you do first?

A. At the tenant level, set Data Activator to Enabled.
B. At the semantic model level, set Large dataset storage format to On.
C. At the tenant level, set Create and use Metrics to Enabled.
D. At the semantic model level, set Large dataset storage format to Off.

正解：B

解説：
To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.

質問 # 13
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?

A. The data type of the column used to partition the data has changed.
B. XMLA Endpoint is set to Read Only.
C. Query folding is occurring.
D. Query folding is NOT occurring.
E. Only refresh complete days is selected.

正解：D

解説：
A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources. Reference = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

質問 # 14
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:
* The code embeds an existing Power BI report. - No
* The code creates a Power BI report. - No
* The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it's likely part of a data processing step that summarizes data.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks

質問 # 15
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint. The query must return the total amount of sales from the same period last year.
How should you complete the DAX expression? To answer, select the appropriate options in the answer are a. NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 16
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 17
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
* Hide the column.
* Set Nullable to False.
* Set Summarize By to None
* Set Available in MDX to False.
* Mark the column as a key column.
What should you use?

A. DAX Studio
B. ALM Toolkit
C. Microsoft Power Bl Desktop
D. Tabular Editor

正解：D

解説：
Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:
Open the model in Tabular Editor.
Create an Advanced Script using C# to iterate over all tables and their respective columns.
Within the script, check if the column name ends with 'Key'.
For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.
Additionally, mark the column as a key column.
Save the changes and deploy them back to the Fabric tenant.

質問 # 18
You have a Fabric tenant that contains a warehouse.
Several times a day. the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.
What should you use to identify whether throttling is occurring?

A. the Microsoft Fabric Capacity Metrics app
B. the Capacity settings
C. dynamic management views (DMVs)
D. the Monitoring hub

正解：D

解説：
To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

質問 # 19
You have an Amazon Web Services (AWS) subscription that contains an Amazon Simple Storage Service (Amazon S3) bucket named bucketl.
You have a Fabric tenant that contains a lakehouse named LH1.
In LH1, you plan to create a OneLake shortcut to bucketl.
You need to configure authentication for the connection.
Which two values should you provide? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. the certificate thumbprint
B. the access ID
C. the secret access key
D. the shared access signature (SAS) token
E. the access key ID

正解：C、E

質問 # 20
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?

A. Yes
B. No

正解：B

質問 # 21
Which syntax should you use in a notebook to access the Research division data for Productlinel?

正解：B

質問 # 22
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

正解：

解説：

Reference:
Introduction to DataFrames - Spark SQL
Power BI and Azure Databricks

質問 # 23
You have a Fabric tenant that contains a lakehouse named Lakehouse1
Readings from 100 loT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily.
All the table and SparkSession settings are set to the default.
You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used.
You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.
What should you do? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:
* Remove the files: Run the VACUUM command on a schedule.
* Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule.
To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.

質問 # 24
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 25
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

A. write
B. show
C. display
D. displayMTML

正解：C

解説：
The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. Reference = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

質問 # 26
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.

What can you identify about the pickupLongitude column?

A. The column has missing values.
B. All the table rows are profiled.
C. There are 935 values that occur only once.
D. The column has duplicate values.

正解：D

解説：
The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. Reference = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.

質問 # 27
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:

Does this meet the goal?