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Braindump Microsoft DP-203 Pdf, DP-203 Latest Test Simulator

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The DP-203 exam is designed for data engineers who are responsible for designing and implementing big data and real-time data solutions using Azure data services. DP-203 exam measures the candidate's ability to design and implement data storage solutions, data processing solutions, and data consumption solutions. DP-203 exam also measures the candidate's ability to monitor and optimize Azure data solutions.

Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is an important certification for IT professionals who work with data solutions on Azure. It tests candidates on their knowledge and skills related to data engineering on Azure and can help them advance their careers and demonstrate their expertise to potential employers.

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The DP-203 exam is designed for data engineers, architects, and developers who want to demonstrate their expertise in Azure data technologies. It is intended for professionals who are responsible for designing and implementing data solutions on Azure, and who have experience working with Azure data services. Candidates who pass the DP-203 Exam will be able to demonstrate their ability to create scalable and secure data solutions that meet the needs of their organization.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q191-Q196):

NEW QUESTION # 191
You have an Azure Data Lake Storage Gen2 account named account1 that stores logs as shown in the following table.

You do not expect that the logs will be accessed during the retention periods.
You need to recommend a solution for account1 that meets the following requirements:
* Automatically deletes the logs at the end of each retention period
* Minimizes storage costs
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.

Answer:

Explanation:

Explanation
Table Description automatically generated

Box 1: Store the infrastructure logs in the Cool access tier and the application logs in the Archive access tier For infrastructure logs: Cool tier - An online tier optimized for storing data that is infrequently accessed or modified. Data in the cool tier should be stored for a minimum of 30 days. The cool tier has lower storage costs and higher access costs compared to the hot tier.
For application logs: Archive tier - An offline tier optimized for storing data that is rarely accessed, and that has flexible latency requirements, on the order of hours. Data in the archive tier should be stored for a minimum of 180 days.
Box 2: Azure Blob storage lifecycle management rules
Blob storage lifecycle management offers a rule-based policy that you can use to transition your data to the desired access tier when your specified conditions are met. You can also use lifecycle management to expire data at the end of its life.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview


NEW QUESTION # 192
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 193
You have an Azure Synapse Analytics dedicated SQL pool.
You run PDW_SHOWSPACEUSED(dbo,FactInternetSales'); and get the results shown in the following table.

Which statement accurately describes the dbo,FactInternetSales table?

  • A. All distribution contain data.
  • B. The table contains less than 1,000 rows.
  • C. The table uses round-robin distribution.
  • D. The table is skewed.

Answer: D

Explanation:
Data skew means the data is not distributed evenly across the distributions.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute


NEW QUESTION # 194
You need to output files from Azure Data Factory.
Which file format should you use for each type of output? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Parquet
Parquet stores data in columns, while Avro stores data in a row-based format. By their very nature, column-oriented data stores are optimized for read-heavy analytical workloads, while row-based databases are best for write-heavy transactional workloads.
Box 2: Avro
An Avro schema is created using JSON format.
AVRO supports timestamps.
Note: Azure Data Factory supports the following file formats (not GZip or TXT).
* Avro format
* Binary format
* Delimited text format
* Excel format
* JSON format
* ORC format
* Parquet format
* XML format
Reference:
https://www.datanami.com/2018/05/16/big-data-file-formats-demystified


NEW QUESTION # 195
You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
Which four 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor


NEW QUESTION # 196
......

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