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[Hardware] 2026 Study DP-203 Demo - Microsoft Data Engineering on Microsoft Azure - High Pa

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【Hardware】 2026 Study DP-203 Demo - Microsoft Data Engineering on Microsoft Azure - High Pa

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What should I know before taking the Microsoft DP-203 exam?Microsoft offers Data Engineering on Microsoft Azure certification to those who wish to demonstrate their knowledge of data engineering on the Microsoft Cloud. The exam comprises multiple-choice questions, and each question is worth one mark. The candidates are required to attempt 60 questions in total, i.e., within 130 minutes. In order to take this exam, the candidates are required to have knowledge of the fundamental concepts related to the subject matter. Knowledge of basic cloud computing concepts (such as virtual machines, virtual networks, etc.) would be beneficial for the candidates. Microsoft DP-203 exam dumps contains an online study guide that explains all the concepts and answers to practice questions. Candidates should try to understand the concepts completely to gain good marks on the test.
Microsoft DP-203 (Data Engineering on Microsoft Azure) Exam is an essential certification for data professionals who are interested in working with data engineering concepts on the Microsoft Azure platform. DP-203 Exam is designed to test an individual's knowledge and understanding of Azure data services, including the Azure Data Factory, Azure Synapse Analytics, and Azure Stream Analytics. The DP-203 certification is an excellent way to demonstrate your expertise in data engineering on Azure and showcase your ability to design and implement data solutions using Azure services.
Microsoft DP-203 exam is an excellent way for professionals to validate their skills in data engineering on the Microsoft Azure platform. Passing DP-203 exam demonstrates that a professional has the knowledge and skills required to design and implement data solutions on Azure, which can lead to career advancement and increased job opportunities in the field of data engineering.
Microsoft Data Engineering on Microsoft Azure Sample Questions (Q247-Q252):NEW QUESTION # 247
You plan to create a real-time monitoring app that alerts users when a device travels more than 200 meters away from a designated location.
You need to design an Azure Stream Analytics job to process the data for the planned app. The solution must minimize the amount of code developed and the number of technologies used.
What should you include in the Stream Analytics job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Diagram, table Description automatically generated
Input type: Stream
You can process real-time IoT data streams with Azure Stream Analytics.
Function: Geospatial
With built-in geospatial functions, you can use Azure Stream Analytics to build applications for scenarios such as fleet management, ride sharing, connected cars, and asset tracking.
Note: In a real-world scenario, you could have hundreds of these sensors generating events as a stream.
Ideally, a gateway device would run code to push these events to Azure Event Hubs or Azure IoT Hubs.
Reference:
https://docs.microsoft.com/en-us ... re-stream-analytics
https://docs.microsoft.com/en-us ... eospatial-scenarios

NEW QUESTION # 248
You are designing a financial transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
* TransactionType: 40 million rows per transaction type
* CustomerSegment: 4 million per customer segment
* TransactionMonth: 65 million rows per month
* AccountType: 500 million per account type
You have the following query requirements:
* Analysts will most commonly analyze transactions for a given month.
* Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
  • A. TransactionMonth
  • B. AccountType
  • C. CustomerSegment
  • D. TransactionType
Answer: D
Explanation:
Explanation
For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributed databases.
Example: Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has
60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.

NEW QUESTION # 249
You have an Azure subscription that contains an Azure Synapse Analytics workspace named workspace1.
Workspace1 contains a dedicated SQL pool named SQL Pool and an Apache Spark pool named sparkpool.
Sparkpool1 contains a DataFrame named pyspark.df.
You need to write the contents of pyspark_df to a tabte in SQLPooM by using a PySpark notebook.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:


NEW QUESTION # 250
You have files and folders in Azure Data Lake Storage Gen2 for an Azure Synapse workspace as shown in the following exhibit.

You create an external table named ExtTable that has LOCATION='/topfolder/'.
When you query ExtTable by using an Azure Synapse Analytics serverless SQL pool, which files are returned?
  • A. File1.csv only
  • B. File1.csv and File4.csv only
  • C. File1.csv, File2.csv, File3.csv, and File4.csv
  • D. File2.csv and File3.csv only
Answer: B
Explanation:
To run a T-SQL query over a set of files within a folder or set of folders while treating them as a single entity or rowset, provide a path to a folder or a pattern (using wildcards) over a set of files or folders.
Reference:
https://docs.microsoft.com/en-us ... le-files-or-folders

NEW QUESTION # 251
You are designing a financial transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
* TransactionType: 40 million rows per transaction type
* CustomerSegment: 4 million per customer segment
* TransactionMonth: 65 million rows per month
* AccountType: 500 million per account type
You have the following query requirements:
* Analysts will most commonly analyze transactions for a given month.
* Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
  • A. AccountType
  • B. TransactionType
  • C. CustomerSegment
  • D. TransactionMonth
Answer: D
Explanation:
Explanation
For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributed databases.
Example: Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has
60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.

NEW QUESTION # 252
......
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