DP-203 Free Practice Test

- (Exam Topic 3)
You develop data engineering solutions for a company.
A project requires the deployment of data to Azure Data Lake Storage.
You need to implement role-based access control (RBAC) so that project members can manage the Azure Data Lake Storage resources.
Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

1. A. Assign Azure AD security groups to Azure Data Lake Storage.
2. B. Configure end-user authentication for the Azure Data Lake Storage account.
3. C. Configure service-to-service authentication for the Azure Data Lake Storage account.
4. D. Create security groups in Azure Active Directory (Azure AD) and add project members.
5. E. Configure access control lists (ACL) for the Azure Data Lake Storage account.

Correct Answer: ADE
References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-secure-data

- (Exam Topic 3)
You have an Azure subscription that contains an Azure Data Lake Storage account. The storage account contains a data lake named DataLake1.
You plan to use an Azure data factory to ingest data from a folder in DataLake1, transform the data, and land the data in another folder.
You need to ensure that the data factory can read and write data from any folder in the DataLake1 file system. The solution must meet the following requirements:
Minimize the risk of unauthorized user access.
Use the principle of least privilege.
Minimize maintenance effort.
How should you configure access to the storage account for the data factory? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solution:
Text Description automatically generated with low confidence
Box 1: Azure Active Directory (Azure AD)
On Azure, managed identities eliminate the need for developers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens.
Box 2: a managed identity
A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Data Lake Storage Gen2 authentication, similar to using your own service principal. It allows this designated factory to access and copy data to or from your Data Lake Storage Gen2.
Note: The Azure Data Lake Storage Gen2 connector supports the following authentication types.
Account key authentication
Service principal authentication
Managed identities for Azure resources authentication Reference:
https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/overview https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You are creating a new notebook in Azure Databricks that will support R as the primary language but will also support Scale and SOL Which switch should you use to switch between languages?

1. A. @<Language>
2. B. %
3. C. \\()
4. D. \\()

Correct Answer: B
To change the language in Databricks’ cells to either Scala, SQL, Python or R, prefix the cell with ‘%’, followed by the language.
%python //or r, scala, sql
Reference:
https://www.theta.co.nz/news-blogs/tech-blog/enhancing-digital-twins-part-3-predictive-maintenance-with-azur

- (Exam Topic 3)
You have an Azure Databricks workspace named workspace! in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster). You need to reduce the time it takes for cluster 1 to start and scale up. The solution must minimize costs. What should you do first?

1. A. Upgrade workspace! to the Premium pricing tier.
2. B. Create a cluster policy in workspace1.
3. C. Create a pool in workspace1.
4. D. Configure a global init script for workspace1.

Correct Answer: C
You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.
Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.
Reference:
https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html

- (Exam Topic 3)
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 and a database named DB1. DB1 contains a fact table named Table1.
You need to identify the extent of the data skew in Table1. What should you do in Synapse Studio?

1. A. Connect to the built-in pool and run dbcc pdw_showspaceused.
2. B. Connect to the built-in pool and run dbcc checkalloc.
3. C. Connect to Pool1 and query sys.dm_pdw_node_scacus.
4. D. Connect to Pool1 and query sys.dm_pdw_nodes_db_partition_scacs.

Correct Answer: A
A quick way to check for data skew is to use DBCC PDW_SHOWSPACEUSED. The following SQL code returns the number of table rows that are stored in each of the 60 distributions. For balanced performance, the rows in your distributed table should be spread evenly across all the distributions.
DBCC PDW_SHOWSPACEUSED('dbo.FactInternetSales'); Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu