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The following DP-203 questions are part of our Microsoft DP-203 real exam questions full version. There are 352 in our DP-203 full version. All of our DP-203 real exam questions can guarantee you success in the first attempt. If you fail DP-203 exam with our Microsoft DP-203 real exam questions, you will get full payment fee refund. Want to practice and study full verion of DP-203 real exam questions? Go now!

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Microsoft DP-203 Exam Actual Questions

The questions for DP-203 were last updated on Feb 21,2025 .

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Question#1

You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1.
You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.
You plan to insert data from the files into Table1 and azure Data Lake Storage Gen2 container named container1.
You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.
You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.
Solution: In an Azure Synapse Analytics pipeline, you use a Get Metadata activity that retrieves the DateTime of the files.
Does this meet the goal?

A. Yes
B. No

Explanation:
Instead use a serverless SQL pool to create an external table with the extra column.
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

Question#2

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 plan to create an Azure Databricks workspace that has a tiered structure.
The workspace will contain the following three workloads:
✑ A workload for data engineers who will use Python and SQL.
✑ A workload for jobs that will run notebooks that use Python, Scala, and SOL.
✑ A workload that data scientists will use to perform ad hoc analysis in Scala and R.
The enterprise architecture team at your company identifies the following standards for Databricks environments:
✑ The data engineers must share a cluster.
✑ The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
✑ All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databricks clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a Standard cluster for the data engineers, and a High Concurrency cluster for the jobs.
Does this meet the goal?

A. Yes
B. No

Explanation:
We need a High Concurrency cluster for the data engineers and the jobs.
Note: Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
Reference: https://docs.azuredatabricks.net/clusters/configure.html

Question#3

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?

A. a materialized view
B. a replicated table
C. in ordered clustered columnstore index
D. result set chaching

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

Question#4

DRAG DROP
You have an Azure Synapse Analytics workspace named WS1.
You have an Azure Data Lake Storage Gen2 container that contains JSON-formatted files in the following format.



You need to use the serverless SQL pool in WS1 to read the files.
How should you complete the Transact-SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.


A. 

Explanation:
Graphical user interface, text, application, email
Description automatically generated
Box 1: openrowset
The easiest way to see to the content of your CSV file is to provide file URL to OPENROWSET function, specify csv FORMAT.
Example:
SELECT *
FROM OPENROWSET(
BULK 'csv/population/population.csv',
DATA_SOURCE = 'SqlOnDemandDemo',
FORMAT = 'CSV', PARSER_VERSION = '2.0',
FIELDTERMINATOR =',',
ROWTERMINATOR = '\n'
Box 2: openjson
You can access your JSON files from the Azure File Storage share by using the mapped drive, as shown in the following example:
SELECT book.* FROM
OPENROWSET(BULK N't:\books\books.json', SINGLE_CLOB) AS json CROSS APPLY OPENJSON(BulkColumn)
WITH( id nvarchar(100), name nvarchar(100), price float, pages_i int, author nvarchar(100)) AS book

Question#5

You are designing a solution that will copy Parquet files stored in an Azure Blob storage account to an Azure Data Lake Storage Gen2 account.
The data will be loaded daily to the data lake and will use a folder structure of {Year}/{Month}/{Day}/.
You need to design a daily Azure Data Factory data load to minimize the data transfer between the two
accounts.
Which two configurations should you include in the design? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. Delete the files in the destination before loading new data.
B. Filter by the last modified date of the source files.
C. Delete the source files after they are copied.
D. Specify a file naming pattern for the destination.

Explanation:
Copy data from one place to another.
The requirements are:
1- need to minimize transfert and 2- need to adapte data to the destination folder structure. Filter on LastModifiedDate will copy everything that have changed since the latest load while minimizing the data transfert. Specifying the file naming pattern allows to copy data at the right place to the destination Data Lake.

Exam Code: DP-203Q & A: 352 Q&AsUpdated:  Feb 21,2025

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