Latest DP-203 Dumps Questions & Exam DP-203 Fees

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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.

The DP-203 Exam measures a candidate's proficiency in areas such as data storage, data processing, and data integration on Azure. Candidates will be evaluated on their ability to implement solutions that meet business requirements, scale with business needs, and ensure data security and compliance. By passing the DP-203 exam, candidates will demonstrate their ability to design and implement data solutions that leverage Azure's advanced analytics and machine learning capabilities, allowing them to provide valuable insights to the organization.

>> Latest DP-203 Dumps Questions <<

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q59-Q64):

NEW QUESTION # 59
You haw an Azure data factory named ADF1.
You currently publish all pipeline authoring changes directly to ADF1.
You need to implement version control for the changes made to pipeline artifacts. The solution must ensure that you can apply version control to the resources currently defined m the UX Authoring canvas for ADF1.
Which two actions should you perform? Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.

A. From the UX Authoring canvas, select Set up code repository
B. Create a GitHub action
C. Create an Azure Data Factory trigger
D. From the Azure Data Factor Studio, run Publish All.
E. From the UX Authoring canvas, select Publish
F. Create a Git repository

Answer: A,F

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/source-control

NEW QUESTION # 60
You have an Azure Synapse Analytics serverless SQL pool, an Azure Synapse Analytics dedicated SQL pool, an Apache Spark pool, and an Azure Data Lake Storage Gen2 account.
You need to create a table in a lake database. The table must be available to both the serverless SQL pool and the Spark pool.
Where should you create the table, and Which file format should you use for data in the table? TO answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
The dedicated SQL pool
Apache Parquet

NEW QUESTION # 61
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?

A. hopping
B. tumbling
C. sliding
D. snapshot

Answer: B

Explanation:
Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

NEW QUESTION # 62
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. an error
B. a null value
C. 0

Answer: A

Explanation:
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 # 63
You have an Azure Synapse Analytics pipeline named Pipeline1 that contains a data flow activity named Dataflow1.
Pipeline1 retrieves files from an Azure Data Lake Storage Gen 2 account named storage1.
Dataflow1 uses the AutoResolveIntegrationRuntime integration runtime configured with a core count of 128.
You need to optimize the number of cores used by Dataflow1 to accommodate the size of the files in storage1.
What should you configure? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation:
Box 1: A Get Metadata activity
Dynamically size data flow compute at runtime
The Core Count and Compute Type properties can be set dynamically to adjust to the size of your incoming source data at runtime. Use pipeline activities like Lookup or Get Metadata in order to find the size of the source dataset data. Then, use Add Dynamic Content in the Data Flow activity properties.
Box 2: Dynamic content
Reference: https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity

NEW QUESTION # 64
......

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Latest DP-203 Dumps Questions & Exam DP-203 Fees

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q59-Q64):

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