Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
Splunk
ServiceNow
All Vendors
  2. Home
  3. Exams
  4. Microsoft
  5. DP-203

Microsoft DP-203 Exam Questions
Data Engineering on Microsoft Azure (Page 3 )

Updated On: 17-Feb-2026
Page 3 of 75
PDF with 397 Questions
View Related Case Study

HOTSPOT (Drag and Drop is not supported)
You need to implement an Azure Synapse Analytics database object for storing the sales transactions data. The solution must meet the sales transaction dataset requirements.

What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Create table
Scenario: Load the sales transaction dataset to Azure Synapse Analytics

Box 2: RANGE RIGHT FOR VALUES
Scenario: Partition data that contains sales transaction records.
Partitions must be designed to provide efficient loads by month.
Boundary values must belong to the partition on the right.
RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values). FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.
Scenario: Load the sales transaction dataset to Azure Synapse Analytics.

Contoso identifies the following requirements for the sales transaction dataset:
-Partition data that contains sales transaction records.
-Partitions must be designed to provide efficient loads by month.
-Boundary values must belong to the partition on the right.
-Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
-Implement a surrogate key to account for changes to the retail store addresses. Ensure that data storage costs and performance are predictable.
-Minimize how long it takes to remove old records.


Reference:

https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse



View Related Case Study

You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.
Which Azure Storage functionality should you include in the solution?

  1. change feed
  2. soft delete
  3. time-based retention
  4. lifecycle management

Answer(s): D

Explanation:

Scenario: Purge Twitter feed data records that are older than two years.
Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.


Reference:

https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview



You have a table in an Azure Synapse Analytics dedicated SQL pool. The table was created by using the following Transact-SQL statement.

You need to alter the table to meet the following requirements:

-Ensure that users can identify the current manager of employees.
-Support creating an employee reporting hierarchy for your entire company.
-Provide fast lookup of the managers’ attributes such as name and job title.

Which column should you add to the table?

  1. [ManagerEmployeeID] [int] NULL
  2. [ManagerEmployeeID] [smallint] NULL
  3. [ManagerEmployeeKey] [int] NULL
  4. [ManagerName] [varchar](200) NULL

Answer(s): C

Explanation:

We need an extra column to identify the Manager. Use the data type as the EmployeeKey column, an int column.


Reference:

https://docs.microsoft.com/en-us/analysis-services/tabular-models/hierarchies-ssas-tabular



You have an Azure Synapse workspace named MyWorkspace that contains an Apache Spark database named mytestdb. You run the following command in an Azure Synapse Analytics Spark pool in MyWorkspace.

CREATE TABLE mytestdb.myParquetTable(
EmployeeID int,
EmployeeName string,
EmployeeStartDate date)
USING Parquet

You then use Spark to insert a row into mytestdb.myParquetTable. The row contains the following data.


One minute later, you execute the following query from a serverless SQL pool in MyWorkspace.

SELECT EmployeeID
FROM mytestdb.dbo.myParquetTable
WHERE name = 'Alice';

What will be returned by the query?

  1. 24
  2. an error
  3. a null value

Answer(s): A

Explanation:

Once a database has been created by a Spark job, you can create tables in it with Spark that use Parquet as the storage format. Table names will be converted to lower case and need to be queried using the lower case name. These tables will immediately become available for querying by any of the Azure Synapse workspace Spark pools. They can also be used from any of the Spark jobs subject to permissions.

Note: For external tables, since they are synchronized to serverless SQL pool asynchronously, there will be a delay until they appear.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/metadata/table



DRAG DROP (Drag and Drop is not supported)
You have a table named SalesFact in an enterprise data warehouse in Azure Synapse Analytics. SalesFact contains sales data from the past 36 months and has the following characteristics:

-Is partitioned by month
-Contains one billion rows
-Has clustered columnstore indexes

At the beginning of each month, you need to remove data from SalesFact that is older than 36 months as quickly as possible.

Which three actions should you perform in sequence in a stored procedure? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Step 1: Create an empty table named SalesFact_work that has the same schema as SalesFact. Step 2: Switch the partition containing the stale data from SalesFact to SalesFact_Work.
SQL Data Warehouse supports partition splitting, merging, and switching. To switch partitions between two tables, you must ensure that the partitions align on their respective boundaries and that the table definitions match.

Loading data into partitions with partition switching is a convenient way stage new data in a table that is not visible to users the switch in the new data. Step 3: Drop the SalesFact_Work table.


Reference:

https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition






Post your Comments and Discuss Microsoft DP-203 exam dumps with other Community members:

Join the DP-203 Discussion