100% Pass Quiz DP-203 - Data Engineering on Microsoft Azure–The Best Reliable Test Prep

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Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is designed for individuals who want to demonstrate their expertise in designing and implementing data solutions on Microsoft Azure. Data Engineering on Microsoft Azure certification exam is ideal for data engineers, data architects, and other IT professionals who are responsible for designing and implementing data solutions on Azure.

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q83-Q88):

NEW QUESTION # 83
You configure version control for an Azure Data Factory instance as 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:

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

NEW QUESTION # 84
You are implementing an Azure Stream Analytics solution to process event data from devices.
The devices output events when there is a fault and emit a repeat of the event every five seconds until the fault is resolved. The devices output a heartbeat event every five seconds after a previous event if there are no faults present.
A sample of the events is shown in the following table.

You need to calculate the uptime between the faults.
How should you complete the Stream Analytics SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application Description automatically generated

Box 1: WHERE EventType='HeartBeat'
Box 2: ,TumblingWindow(Second, 5)
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.
Timeline Description automatically generated

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

NEW QUESTION # 85
You have an Azure event hub named retailhub that has 16 partitions. Transactions are posted to retailhub.
Each transaction includes the transaction ID, the individual line items, and the payment details. The transaction ID is used as the partition key.
You are designing an Azure Stream Analytics job to identify potentially fraudulent transactions at a retail store. The job will use retailhub as the input. The job will output the transaction ID, the individual line items, the payment details, a fraud score, and a fraud indicator.
You plan to send the output to an Azure event hub named fraudhub.
You need to ensure that the fraud detection solution is highly scalable and processes transactions as quickly as possible.
How should you structure the output of the Stream Analytics job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: 16
For Event Hubs you need to set the partition key explicitly.
An embarrassingly parallel job is the most scalable scenario in Azure Stream Analytics. It connects one partition of the input to one instance of the query to one partition of the output.
Box 2: Transaction ID
Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#partitions

NEW QUESTION # 86
You are incrementally loading data into fact tables in an Azure Synapse Analytics dedicated SQL pool.
Each batch of incoming data is staged before being loaded into the fact tables. | You need to ensure that the incoming data is staged as quickly as possible. | How should you configure the staging tables? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation

Round-robin distribution is recommended for staging tables because it distributes data evenly across all the distributions without requiring a hash column. This can improve the speed of data loading and avoid data skew. Heap tables are recommended for staging tables because they do not have any indexes or partitions that can slow down the data loading process. Heap tables are also easier to truncate and reload than clustered index or columnstore index tables.

NEW QUESTION # 87
You have an Azure Data Lake Storage account that contains a staging zone.
You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.
Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data Flow, and then inserts the data info the data warehouse.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not a mapping flow,5 with your own data processing logic and use the activity in the pipeline. You can create a custom activity to run R scripts on your HDInsight cluster with R installed.
Reference:
https://docs.microsoft.com/en-US/azure/data-factory/transform-data

NEW QUESTION # 88
......

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100% Pass Quiz DP-203 - Data Engineering on Microsoft Azure–The Best Reliable Test Prep

DP-203 Exam Online & DP-203 Reliable Braindumps Sheet

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q83-Q88):

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