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Free DP-900 Exam Braindumps (page: 4)

Page 3 of 72
PDF with 306 Questions

HOTSPOT (Drag and Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/massively-parallel-processing-mpp-architecture



HOTSPOT (Drag and Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-described?view=sql-server-ver15



HOTSPOT (Drag and Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Disadvantages of non-relational databases include: Data Consistency ג€" non-relational databases do not perform ACID transactions.
Note: Relational databases are optimized for writes. They are optimized for consistency and availability. Advantages of relational databases include simplicity, ease of data retrieval, data integrity, and flexibility.

Incorrect Answers:
Use a relational database when data that you work with is structured, and the structure is not subject to frequent changes.
Use Cloud storage (no relational database) for geographically distributed writes.


Reference:

https://towardsdatascience.com/choosing-the-right-database-c45cd3a28f77



HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Yes
Big data solutions often use long-running batch jobs to filter, aggregate, and otherwise prepare the data for analysis. Usually these jobs involve reading source files from scalable storage (like HDFS, Azure Data Lake Store, and Azure Storage), processing them, and writing the output to new files in scalable storage.
Box 2: No
Box 3: No


Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/batch-processing






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