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DP-203 exam braindumps: Data Engineering on Microsoft Azure & DP-203 study guide

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The Microsoft DP-203 Exam measures a candidate's ability to develop solutions using Azure services like Azure Data Lake Storage, Azure Synapse Analytics, Azure Databricks, and Azure Stream Analytics. A candidate's knowledge on how to design and implement data processing solutions on Azure is also evaluated in the exam. Data Engineering on Microsoft Azure certification is ideal for data engineers who wish to demonstrate their ability to design and implement data solutions using Microsoft Azure services.

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q241-Q246):

NEW QUESTION # 241
You have an Azure subscription that contains an Azure Databricks workspace named databricks1 and an Azure Synapse Analytics workspace named synapse1. The synapse1 workspace contains an Apache Spark pool named pool1.
You need to share an Apache Hive catalog of pool1 with databricks1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Azure SQL Database
Use external Hive Metastore for Synapse Spark Pool
Azure Synapse Analytics allows Apache Spark pools in the same workspace to share a managed HMS (Hive Metastore) compatible metastore as their catalog.
Set up linked service to Hive Metastore
Follow below steps to set up a linked service to the external Hive Metastore in Synapse workspace.
* Open Synapse Studio, go to Manage > Linked services at left, click New to create a new linked service.
* Set up Hive Metastore linked service
* Choose Azure SQL Database or Azure Database for MySQL based on your database type, click Continue.
* Provide Name of the linked service. Record the name of the linked service, this info will be used to configure Spark shortly.
* You can either select Azure SQL Database/Azure Database for MySQL for the external Hive Metastore from Azure subscription list, or enter the info manually.
* Provide User name and Password to set up the connection.
* Test connection to verify the username and password.
* Click Create to create the linked service.
Box 2: A Hive Metastore
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-external-metastore

NEW QUESTION # 242
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 dat a. 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 data flow that contains a Derived Column transformation.

A. Yes
B. No

Answer: A

Explanation:
Use the derived column transformation to generate new columns in your data flow or to modify existing fields.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-derived-column

NEW QUESTION # 243
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.

A. Add your Azure Active Directory (Azure AD) account to the Sales group.
B. Create a managed identity.
C. Use the snared access signature (SAS) as the credentials for the data load process.
D. Add the managed identity to the Sales group.
E. Create a shared access signature (SAS).
F. Use the managed identity as the credentials for the data load process.

Answer: A,B,D

Explanation:
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

NEW QUESTION # 244
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains an external table named Sales. Sales contains sales data. Each row in Sales contains data on a single sale, including the name of the salesperson.
You need to implement row-level security (RLS). The solution must ensure that the salespeople can access only their respective sales.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: A security policy for sale
Here are the steps to create a security policy for Sales:
* Create a user-defined function that returns the name of the current user:
* CREATE FUNCTION dbo.GetCurrentUser()
* RETURNS NVARCHAR(128)
* AS
* BEGIN
* RETURN SUSER_SNAME();
* END;
* Create a security predicate function that filters the Sales table based on the current user:
* CREATE FUNCTION dbo.SalesPredicate(@salesperson NVARCHAR(128))
* RETURNS TABLE
* WITH SCHEMABINDING
* AS
* RETURN SELECT 1 AS access_result
* WHERE @salesperson = SalespersonName;
* Create a security policy on the Sales table that uses the SalesPredicate function to filter the data:
* CREATE SECURITY POLICY SalesFilter
* ADD FILTER PREDICATE dbo.SalesPredicate(dbo.GetCurrentUser()) ON dbo.Sales
* WITH (STATE = ON);
By creating a security policy for the Sales table, you ensure that each salesperson can only access their own sales data. The security policy uses a user-defined function to get the name of the current user and a security predicate function to filter the Sales table based on the current user.
Box 2: table-value function
to restrict row access by using row-level security, you need to create a table-valued function that returns a table of values that represent the rows that a user can access. You then use this function in a security policy that applies a predicate on the table.

NEW QUESTION # 245
You have an Azure Data Factory pipeline that has the activities shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Box 1: succeed
Box 2: failed
Example:
Now let's say we have a pipeline with 3 activities, where Activity1 has a success path to Activity2 and a failure path to Activity3. If Activity1 fails and Activity3 succeeds, the pipeline will fail. The presence of the success path alongside the failure path changes the outcome reported by the pipeline, even though the activity executions from the pipeline are the same as the previous scenario.

Activity1 fails, Activity2 is skipped, and Activity3 succeeds. The pipeline reports failure.
Reference:
https://datasavvy.me/2021/02/18/azure-data-factory-activity-failures-and-pipeline-outcomes/

NEW QUESTION # 246
......

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