[2025年02月21日] 完全版最新のDP-600問題集で100%カバー率問題と解答があなたをリアル試験で合格させる [Q30-Q50]

[2025年02月21日] 完全版最新のDP-600問題集で100%カバー率問題と解答があなたをリアル試験で合格させる

最新DP-600試験問題集で有効最新の問題集

質問 # 30
You need to recommend a solution to group the Research division workspaces.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 31
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do?

A. Create and schedule a Spark notebook.
B. Create a dataflow that has multiple steps and schedule the dataflow.
C. Create a pipeline that has dependencies between activities and schedule the pipeline.
D. Create and schedule a Spark job definition.

正解：C

解説：
To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

質問 # 32
You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dawset named DS1. You need to ensure read-write access to DS1 is available by using the XMLA endpoint. What should be modified first?

A. the DS1 settings
B. the Tenant1 settings
C. the C1 settings
D. the WS1 settings

正解：C

解説：
To ensure read-write access to DS1 is available by using the XMLA endpoint, the C1 settings (which refer to the capacity settings) should be modified first. XMLA endpoint configuration is a capacity feature, not specific to individual datasets or workspaces. References = The configuration of XMLA endpoints in Power BI capacities is detailed in the Power BI documentation on dataset management.

質問 # 33
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files. You need to convert the CSV files into the delta format that has V-Order optimization enabled. What should you do from Lakehouse explorer?

A. Create a new shortcut in the Tables section.
B. Use the Optimize feature.
C. Create a new shortcut in the Files section.
D. Use the Load to Tables feature.

正解：B

解説：
To convert CSV files into the delta format with Z-Order optimization enabled, you should use the Optimize feature (D) from Lakehouse Explorer. This will allow you to optimize the file organization for the most efficient querying. References = The process for converting and optimizing file formats within a lakehouse is discussed in the lakehouse management documentation.

質問 # 34
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1.
You are creating a new data pipeline.
You plan to copy external data to Table1. The schema of the external data changes regularly.
You need the copy operation to meet the following requirements:
* Replace Table1 with the schema of the external data.
* Replace all the data in Table1 with the rows in the external data.
You add a Copy data activity to the pipeline. What should you do for the Copy data activity?

A. From the Source tab, select Enable partition discovery
B. From the Settings tab, select Enable staging
C. From the Source tab, add additional columns.
D. From the Source tab, select Recursively
E. From the Destination tab, set Table action to Overwrite.

正解：E

解説：
For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table. References = Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.

質問 # 35
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
EXPLAIN TABLE customer
Does this meet the goal?

A. No
B. Yes

正解：A

解説：
No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table.
It shows the execution plan for a query. References = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.

質問 # 36
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

正解：A

解説：
The correct code to populate the results DataFrame with minimal data shuffling is Option A.
Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. Reference = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

質問 # 37
You have a Microsoft Power Bl semantic model.
You need to identify any surrogate key columns in the model that have the Summarize By property set to a value other than to None. The solution must minimize effort.
What should you use?

A. Model view in Microsoft Power Bl Desktop
B. DAX Formatter in DAX Studio
C. Best Practice Analyzer in Tabular Editor
D. Model explorer in Microsoft Power Bl Desktop

正解：C

解説：
To identify surrogate key columns with the "Summarize By" property set to a value other than "None," the Best Practice Analyzer in Tabular Editor is the most efficient tool. The Best Practice Analyzer can analyze the entire model and provide a report on all columns that do not meet a specified best practice, such as having the "Summarize By" property set correctly for surrogate key columns. Here's how you would proceed:
Open your Power BI model in Tabular Editor.
Go to the Advanced Scripting window.
Write or use an existing script that checks the "Summarize By" property of each column.
Execute the script to get a report on the surrogate key columns that do not have their "Summarize By" property set to "None".
You can then review and adjust the properties of the columns directly within the Tabular Editor.

質問 # 38
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation:
To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:
* From Performance analyzer, capture a recording.
* View the Server Timings tab.
* Enable Query Timings and Server Timings. Run the query.
* View the Query Timings tab.
* Sort the Duration (ms) column in descending order by DAX query time.

質問 # 39
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. - Yes
* 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 # 40
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：

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

質問 # 41
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.

You need to write a T-SQL query that will return the following columns.

How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:

For the PeriodDate that returns the first day of the month for OrderDate, you should use DATEFROMPARTS as it allows you to construct a date from its individual components (year, month, day).
For the DayName that returns the name of the day for OrderDate, you should use DATENAME with the weekday date part to get the full name of the weekday.
The complete SQL query should look like this:
SELECT OrderID, CustomerID,
DATEFROMPARTS(YEAR(OrderDate), MONTH(OrderDate), 1) AS PeriodDate,
DATENAME(weekday, OrderDate) AS DayName
FROM Sales.Orders
Select DATEFROMPARTS for the PeriodDate and weekday for the DayName in the answer area.

質問 # 42
You need to recommend a solution to prepare the tenant for the PoC.
Which two actions should you recommend performing from the Fabric Admin portal? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

A. Enable the Users can try Microsoft Fabric paid features option for specific security groups.
B. Enable the Users can create Fabric items option for specific security groups.
C. Enable the Users can create Fabric items option and exclude specific security groups.
D. Enable the Allow Azure Active Directory guest users to access Microsoft Fabric option for specific security groups.
E. Enable the Users can try Microsoft Fabric paid features option for the entire organization.

正解：A、B

解説：
The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A).
The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).

質問 # 43
You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. query caching
B. automatic aggregation
C. user-defined aggregations
D. OneLake integration

正解：A、D

解説：
User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.

質問 # 44
You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

A. Group by
B. Unpivot columns
C. Split column
D. Remove other columns
E. Unpivot other columns

正解：B

解説：
The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.

質問 # 45
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Reference:
Dimensional modeling
Star schema design

質問 # 46
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

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

質問 # 47
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?

A. From the Destination tab, set Mode to Append.
B. From the Source tab, select Enable partition discovery
C. From the Destination tab, select the partition column,
D. From the Destination tab, set Mode to Overwrite.

正解：D

解説：
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. Reference = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.

質問 # 48
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows. You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;
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 # 49
You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS). You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse. When users interact with a report built from the model, which mode will be used by the DAX queries?