DP-700基礎問題集 & DP-700実際試験

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今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。DP-700認定試験はMicrosoftの中に重要な認証試験の一つですが、Pass4TestにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってMicrosoft DP-700「Implementing Data Engineering Solutions Using Microsoft Fabric」認証試験に参加する方に対して問題集を研究続けています。

Microsoft DP-700 認定試験の出題範囲：

トピック出題範囲
トピック 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.
トピック 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.
トピック 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.

>> DP-700基礎問題集 <<

Microsoft DP-700実際試験、DP-700試験内容

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric 認定 DP-700 試験問題 (Q145-Q150):

質問 # 145
You have a KQL database that contains a table named Readings.
You need to build a KQL query to compare the Meter-Reading value of each row to the previous row base on the ilmestamp value A sample of the expected output is shown in the following table.

正解：

解説：

Explanation:


質問 # 146
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to Workspace1. You create a table by running the following statement.

You need to ensure that the team can view only the first two characters and the last four characters of the Creditcard attribute.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 147
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 KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.
You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.
Solution: You add the make_list() function to the output columns.
Does this meet the goal?

正解：A

解説：
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.


質問 # 148
You have a Fabric workspace that contains a warehouse named Warehouse1 Warehousel contains a table named DimCustomers. DimCustomers contains the following columns:
* CustomerName
* CustomerlD
* Birth Date
* Email
You need to configure security to meet the following requirements:
* Birth Date in DimCustomer must be masked and display 1960-01-01
* Email in DimCustomer must be masked and display only the first leading character and the last five characters.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 149
You have a Fabric workspace that contains a lakehouse named Lakehousel. Lakehousel contains a table named Status_Target that has the following columns:
* Key
* Status
* LastModified
The data source contains a table named Status.Source that has the same columns as Status_Target. Status.
Source is used to populate Status_Target. In a notebook name Notebook!, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF. You need to implement an incremental loading pattern by using Notebook-!. The solution must meet the following requirements:
* For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
* Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
* Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status.Source.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 150
......

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