100% PASS 2025 AUTHORITATIVE DP-203: DATA ENGINEERING ON MICROSOFT AZURE QUESTIONS EXAM

100% Pass 2025 Authoritative DP-203: Data Engineering on Microsoft Azure Questions Exam

100% Pass 2025 Authoritative DP-203: Data Engineering on Microsoft Azure Questions Exam

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q316-Q321):

NEW QUESTION # 316
You have an Azure Synapse Analytics dedicated SQL pool named pool1.
You plan to implement a star schema in pool1 and create a new table named DimCustomer by using the following code.

You need to ensure that DimCustomer has the necessary columns to support a Type 2 slowly changing dimension (SCD). Which two columns should you add? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  • A. [RowID] [bigint] NOT NULL
  • B. [PreviousModifiedDate] [datetime] NOT NULL
  • C. [HistoricalSalesPerson] [nvarchar] (256) NOT NULL
  • D. [EffectiveStartDate] [datetime] NOT NULL
  • E. [EffectiveEndDate] [datetime] NOT NULL

Answer: C,E


NEW QUESTION # 317
You have the Azure Synapse Analytics pipeline shown in the following exhibit.

You need to add a set variable activity to the pipeline to ensure that after the pipeline's completion, the status of the pipeline is always successful.
What should you configure for the set variable activity?

  • A. a skipped dependency on the Upon Success activity
  • B. a failure dependency on the Upon Failure activity
  • C. a success dependency on the Business Activity That Fails activity
  • D. a skipped dependency on the Upon Failure activity

Answer: B

Explanation:
A failure dependency means that the activity will run only if the previous activity fails. In this case, setting a failure dependency on the Upon Failure activity will ensure that the set variable activity will run after the pipeline fails and set the status of the pipeline to successful.


NEW QUESTION # 318
You use Azure Data Factory to prepare data to be queried by Azure Synapse Analytics serverless SQL pools.
Files are initially ingested into an Azure Data Lake Storage Gen2 account as 10 small JSON files. Each file contains the same data attributes and data from a subsidiary of your company.
You need to move the files to a different folder and transform the data to meet the following requirements:
Provide the fastest possible query times.
Automatically infer the schema from the underlying files.
How should you configure the Data Factory copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Preserver herarchy
Compared to the flat namespace on Blob storage, the hierarchical namespace greatly improves the performance of directory management operations, which improves overall job performance.
Box 2: Parquet
Azure Data Factory parquet format is supported for Azure Data Lake Storage Gen2.
Parquet supports the schema property.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
https://docs.microsoft.com/en-us/azure/data-factory/format-parquet


NEW QUESTION # 319
You build an Azure Data Factory pipeline to move data from an Azure Data Lake Storage Gen2 container to a database in an Azure Synapse Analytics dedicated SQL pool.
Data in the container is stored in the following folder structure.
/in/{YYYY}/{MM}/{DD}/{HH}/{mm}
The earliest folder is /in/2021/01/01/00/00. The latest folder is /in/2021/01/15/01/45.
You need to configure a pipeline trigger to meet the following requirements:
* Existing data must be loaded.
* Data must be loaded every 30 minutes.
* Late-arriving data of up to two minutes must he included in the load for the time at which the data should have arrived.
How should you configure the pipeline trigger? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Tumbling window
To be able to use the Delay parameter we select Tumbling window.
Box 2:
Recurrence: 30 minutes, not 32 minutes
Delay: 2 minutes.
The amount of time to delay the start of data processing for the window. The pipeline run is started after the expected execution time plus the amount of delay. The delay defines how long the trigger waits past the due time before triggering a new run. The delay doesn't alter the window startTime.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger


NEW QUESTION # 320
You are planning the deployment of Azure Data Lake Storage Gen2.
You have the following two reports that will access the data lake:
Report1: Reads three columns from a file that contains 50 columns.
Report2: Queries a single record based on a timestamp.
You need to recommend in which format to store the data in the data lake to support the reports. The solution must minimize read times.
What should you recommend for each report? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://streamsets.com/documentation/datacollector/latest/help/datacollector/UserGuide/Destinations/ADLS-G2-D.html


NEW QUESTION # 321
......

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