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  3. DP-203

Microsoft DP-203 Exam (page: 4)
Microsoft Data Engineering on Azure
Updated on: 28-Jul-2025

Viewing Page 4 of 75
DP-203 PDF 397 Questions

HOTSPOT (Drag and Drop is not supported)
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.



All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB. The dimension tables will be relatively static with very few data inserts and updates.

Which type of table should you use for each table? 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: Replicated
Replicated tables are ideal for small star-schema dimension tables, because the fact table is often distributed on a column that is not compatible with the connected dimension tables. If this case applies to your schema, consider changing small dimension tables currently implemented as round-robin to replicated.

Box 2: Replicated

Box 3: Replicated

Box 4: Hash-distributed
For Fact tables use hash-distribution with clustered columnstore index. Performance improves when two hash tables are joined on the same distribution column.


Reference:

https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-the-general-availability-of-replicated- tables/
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/



HOTSPOT (Drag and Drop is not supported)
You have an Azure Data Lake Storage Gen2 container.

Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.

You need to design a data archiving solution that meets the following requirements:

-New data is accessed frequently and must be available as quickly as possible.
-Data that is older than five years is accessed infrequently but must be available within one second when requested.
-Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
-Costs must be minimized while maintaining the required availability.

How should you manage the data? 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: Move to cool storage Box 2: Move to archive storage
Archive - Optimized for storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements, on the order of hours. The following table shows a comparison of premium performance block blob storage, and the hot, cool, and archive access tiers.


Reference:

https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage-tiers



DRAG DROP (Drag and Drop is not supported)
You need to create a partitioned table in an Azure Synapse Analytics dedicated SQL pool.

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.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: DISTRIBUTION
Table distribution options include DISTRIBUTION = HASH ( distribution_column_name ), assigns each row to one distribution by hashing the value stored in distribution_column_name.

Box 2: PARTITION
Table partition options. Syntax:
PARTITION ( partition_column_name RANGE [ LEFT | RIGHT ] FOR VALUES ( [ boundary_value [,...n] ] ))


Reference:

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



You need to design an Azure Synapse Analytics dedicated SQL pool that meets the following requirements:

-Can return an employee record from a given point in time.
-Maintains the latest employee information.
-Minimizes query complexity.

How should you model the employee data?

  1. as a temporal table
  2. as a SQL graph table
  3. as a degenerate dimension table
  4. as a Type 2 slowly changing dimension (SCD) table

Answer(s): D

Explanation:

A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.


Reference:

https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-pipelines/3-choose-between-dimension-types



You have an enterprise-wide Azure Data Lake Storage Gen2 account. The data lake is accessible only through an Azure virtual network named VNET1.

You are building a SQL pool in Azure Synapse that will use data from the data lake.
Your company has a sales team. All the members of the sales team are in an Azure Active Directory group named Sales. POSIX controls are used to assign the Sales group access to the files in the data lake.

You plan to load data to the SQL pool every hour.
You need to ensure that the SQL pool can load the sales data from the data lake.

Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each area selection is worth one point.

  1. Add the managed identity to the Sales group.
  2. Use the managed identity as the credentials for the data load process.
  3. Create a shared access signature (SAS).
  4. Add your Azure Active Directory (Azure AD) account to the Sales group.
  5. Use the snared access signature (SAS) as the credentials for the data load process.
  6. Create a managed identity.

Answer(s): A,B,F

Explanation:

The managed identity grants permissions to the dedicated SQL pools in the workspace.
Note: Managed identity for Azure resources is a feature of Azure Active Directory. The feature provides Azure services with an automatically managed identity in Azure AD


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/security/synapse-workspace-managed-identity



Viewing Page 4 of 75



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