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DP-203 Certification Training and DP-203 Test Torrent - Data Engineering on Microsoft Azure Guide Torrent - PDFBraindumps

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To prepare for the Microsoft DP-203 certification exam, candidates can access various resources, including online training courses, study guides, practice tests, and hands-on experience working with Azure data services. Microsoft also provides an official certification exam guide that outlines the exam's objectives, format, and recommended study materials. Candidates can register for the exam through the Microsoft Certification website and take the exam at an authorized testing center or online through Pearson VUE.

Microsoft DP-203: Data Engineering on Microsoft Azure is a certification exam that validates the skills and knowledge of professionals in data engineering on Azure. DP-203 Exam covers various topics such as data storage, data processing, data transformation, and data integration using Azure services. DP-203 exam is suitable for data engineers who work with Azure services such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics. Data Engineering on Microsoft Azure certification is globally recognized and is highly valued by employers, making it an excellent choice for professionals who want to advance their careers in data engineering.

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Microsoft DP-203 Exam is suitable for those who have experience with Microsoft Azure and want to specialize in data engineering. DP-203 exam covers a wide range of topics such as data processing, data storage, data transformation, and data integration. It tests the candidate's ability to design, implement, and monitor data processing solutions on Azure.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q67-Q72):

NEW QUESTION # 67
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?

A. a null value
B. an error
C. 0

Answer: B

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

NEW QUESTION # 68
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:

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

NEW QUESTION # 69
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 # 70
You have an enterprise data warehouse in Azure Synapse Analytics named DW1 on a server named Server1.
You need to determine the size of the transaction log file for each distribution of DW1.
What should you do?

A. On DW1, execute a query against the sys.database_files dynamic management view.
B. From Azure Monitor in the Azure portal, execute a query against the logs of DW1.
C. Execute a query against the logs of DW1 by using the
Get-AzOperationalInsightsSearchResult PowerShell cmdlet.
D. On the master database, execute a query against the
sys.dm_pdw_nodes_os_performance_counters dynamic management view.

Answer: A

Explanation:
Explanation
For information about the current log file size, its maximum size, and the autogrow option for the file, you can also use the size, max_size, and growth columns for that log file in sys.database_files.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/logs/manage-the-size-of-the-transaction-log-file

NEW QUESTION # 71
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Convert the query to a reference query.
B. Add a pass-through query.
C. Increase the number of streaming units.
D. Scale out the query by using PARTITION BY.
E. Add a temporal analytic function.

Answer: C,D

NEW QUESTION # 72
......

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