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  3. DP-203

Microsoft DP-203 Exam (page: 7)
Microsoft Data Engineering on Azure
Updated on: 28-Jul-2025

Viewing Page 7 of 75
DP-203 PDF 397 Questions

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 an Azure Storage account that contains 100 GB of files. The files contain rows of text and numerical values. 75% of the rows containdescription data that has an average length of 1.1 MB.

You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics. You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is more than 1 MB. Does this meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

Instead convert the files to compressed delimited text files.


Reference:

https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data



You build a data warehouse in an Azure Synapse Analytics dedicated SQL pool.
Analysts write a complex SELECT query that contains multiple JOIN and CASE statements to transform data for use in inventory reports. The inventory reports will use the data and additional WHERE parameters depending on the report. The reports will be produced once daily.
You need to implement a solution to make the dataset available for the reports. The solution must minimize query times.
What should you implement?

  1. an ordered clustered columnstore index
  2. a materialized view
  3. result set caching
  4. a replicated table

Answer(s): B

Explanation:

Materialized views for dedicated SQL pools in Azure Synapse provide a low maintenance method for complex analytical queries to get fast performance without any query change.

Incorrect Answers:
C: One daily execution does not make use of result cache caching.
Note: When result set caching is enabled, dedicated SQL pool automatically caches query results in the user database for repetitive use. This allows subsequent query executions to get results directly from the persisted cache so recomputation is not needed. Result set caching improves query performance and reduces compute resource usage. In addition, queries using cached results set do not use any concurrency slots and thus do not count against existing concurrency limits.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuningmaterialized- views
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-result-setcaching



You have an Azure Synapse Analytics workspace named WS1 that contains an Apache Spark pool named Pool1.
You plan to create a database named DB1 in Pool1.
You need to ensure that when tables are created in DB1, the tables are available automatically as external tables to the built-in serverless SQL pool.
Which format should you use for the tables in DB1?

  1. CSV
  2. ORC
  3. JSON
  4. Parquet

Answer(s): D

Explanation:

Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools.
For each Spark external table based on Parquet or CSV and located in Azure Storage, an external table is created in a serverless SQL pool database.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-storage-files-spark-tables



You are planning a solution to aggregate streaming data that originates in Apache Kafka and is output to Azure Data Lake Storage Gen2. The developers who will implement the stream processing solution use Java. Which service should you recommend using to process the streaming data?

  1. Azure Event Hubs
  2. Azure Data Factory
  3. Azure Stream Analytics
  4. Azure Databricks

Answer(s): D

Explanation:

The following tables summarize the key differences in capabilities for stream processing technologies in Azure.


General capabilities


Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/stream-processing



You plan to implement an Azure Data Lake Storage Gen2 container that will contain CSV files. The size of the files will vary based on the number of events that occur per hour.
File sizes range from 4 KB to 5 GB.
You need to ensure that the files stored in the container are optimized for batch processing. What should you do?

  1. Convert the files to JSON
  2. Convert the files to Avro
  3. Compress the files
  4. Merge the files

Answer(s): D



Viewing Page 7 of 75



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