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The DP-203 exam covers several key topics, including designing and implementing data storage solutions, ingesting and transforming data, processing and analyzing data, and monitoring and optimizing data solutions. It also tests candidates' knowledge of Azure data services such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Cosmos DB, among others. Successful completion of DP-203 exam demonstrates that a candidate has the skills and knowledge to design and implement data solutions that meet the business requirements of their organization.
Microsoft DP-203 Certification Exam is designed to test your skills and knowledge in data engineering on the Microsoft Azure platform. Data Engineering on Microsoft Azure certification is ideal for data engineers who want to leverage Azure data services to design and implement scalable data solutions. DP-203 exam measures your ability to design and implement data storage solutions, manage data processing, and monitor and optimize data solutions.
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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q207-Q212):
NEW QUESTION # 207
You have an Azure subscription that contains an Azure data factory.
You are editing an Azure Data Factory activity JSON.
The script needs to copy a file from Azure Blob Storage to multiple destinations. The solution must ensure that the source and destination files have consistent folder paths.
How should you complete the script? 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
Answer:
Explanation:
NEW QUESTION # 208
You have an Azure SQL database named Database1 and two Azure event hubs named HubA and HubB. The data consumed from each source is shown in the following table.
You need to implement Azure Stream Analytics to calculate the average fare per mile by driver.
How should you configure the Stream Analytics input for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
HubA: Stream
HubB: Stream
Database1: Reference
Reference data (also known as a lookup table) is a finite data set that is static or slowly changing in nature, used to perform a lookup or to augment your data streams. For example, in an IoT scenario, you could store metadata about sensors (which don't change often) in reference data and join it with real time IoT data streams. Azure Stream Analytics loads reference data in memory to achieve low latency stream processing Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
NEW QUESTION # 209
You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation
Step 1: Create an Azure Storage account that has a lifecycle policy
To automate common data management tasks, Microsoft created a solution based on Azure Data Factory. The service, Data Lifecycle Management, makes frequently accessed data available and archives or purges other data according to retention policies. Teams across the company use the service to reduce storage costs, improve app performance, and comply with data retention policies.
Step 2: Create a Log Analytics workspace that has Data Retention set to 120 days.
Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis to multiple different targets, such as a Storage Account: Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
Step 3: From Azure Portal, add a diagnostic setting.
Step 4: Send the data to a log Analytics workspace,
Event Hub: A pipeline that transfers events from services to Azure Data Explorer.
Keeping Azure Data Factory metrics and pipeline-run data.
Configure diagnostic settings and workspace.
Create or add diagnostic settings for your data factory.
In the portal, go to Monitor. Select Settings > Diagnostic settings.
Select the data factory for which you want to set a diagnostic setting.
If no settings exist on the selected data factory, you're prompted to create a setting. Select Turn on diagnostics.
Give your setting a name, select Send to Log Analytics, and then select a workspace from Log Analytics Workspace.
Select Save.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor
NEW QUESTION # 210
You are designing a slowly changing dimension (SCD) for supplier data in an Azure Synapse Analytics dedicated SQL pool.
You plan to keep a record of changes to the available fields.
The supplier data contains the following columns.
Which three additional columns should you add to the data to create a Type 2 SCD? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/integration-services/data-flow/transformations/slowly-changing-dimension-
NEW QUESTION # 211
You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.
How should you complete the Data Factory data flow script? 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.
Answer:
Explanation:
Explanation
The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.
Box 1: dept=='ecommerce', dept=='retail', dept=='wholesale'
First we put the condition. The order must match the stream labeling we define in Box 3.
Syntax:
<incomingStream>
split(
<conditionalExpression1>
<conditionalExpression2>
disjoint: {true | false}
) ~> <splitTx>@(stream1, stream2, ..., <defaultStream>)
Box 2: discount : false
disjoint is false because the data goes to the first matching condition. All remaining rows matching the third condition go to output stream all.
Box 3: ecommerce, retail, wholesale, all
Label the streams
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-conditional-split
NEW QUESTION # 212
......
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