現在の急速的な発展に伴い、人材に対する要求がますます高くなってきます。国際的なDP-203認定試験資格証明書を持たれば、多くの求職者の中できっと目立っています。私たちのDP-203問題集はあなたの競争力を高めることができます。つまり、私たちのDP-203問題集を利用すれば、DP-203認定試験資格証明書を取ることができます。それはちょうどあなたがもらいたい物ではないでしょうか?
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It-PassportsのDP-203問題集を利用してみたらどうですか。この問題集は最近更新されたもので、実際試験で出題される可能性がある問題をすべて含んでいて、あなたが一回で成功することを保証できますから。この問題集は信じられないほどの良い成果を見せます。試験に失敗すればIt-Passportsは全額返金のことができますから、ご安心に問題集を利用してください。It-PassportsのDP-203試験参考書できっとあなたが望ましい成功を取られます。
質問 # 124
You have an Azure Stream Analytics job that is a Stream Analytics project solution in Microsoft Visual Studio. The job accepts data generated by IoT devices in the JSON format.
You need to modify the job to accept data generated by the IoT devices in the Protobuf format.
Which three actions should you perform from Visual Studio on sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解:
解説:
Explanation
Step 1: Add an Azure Stream Analytics Custom Deserializer Project (.NET) project to the solution.
Create a custom deserializer
1. Open Visual Studio and select File > New > Project. Search for Stream Analytics and select Azure Stream Analytics Custom Deserializer Project (.NET). Give the project a name, like Protobuf Deserializer.
2. In Solution Explorer, right-click your Protobuf Deserializer project and select Manage NuGet Packages from the menu. Then install the Microsoft.Azure.StreamAnalytics and Google.Protobuf NuGet packages.
3. Add the MessageBodyProto class and the MessageBodyDeserializer class to your project.
4. Build the Protobuf Deserializer project.
Step 2: Add .NET deserializer code for Protobuf to the custom deserializer project Azure Stream Analytics has built-in support for three data formats: JSON, CSV, and Avro. With custom .NET deserializers, you can read data from other formats such as Protocol Buffer, Bond and other user defined formats for both cloud and edge jobs.
Step 3: Add an Azure Stream Analytics Application project to the solution Add an Azure Stream Analytics project
* In Solution Explorer, right-click the Protobuf Deserializer solution and select Add > New Project. Under Azure Stream Analytics > Stream Analytics, choose Azure Stream Analytics Application. Name it ProtobufCloudDeserializer and select OK.
* Right-click References under the ProtobufCloudDeserializer Azure Stream Analytics project. Under Projects, add Protobuf Deserializer. It should be automatically populated for you.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/custom-deserializer
質問 # 125
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.
正解:B
解説:
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
質問 # 126
You have an Azure Data Factory pipeline that is triggered hourly.
The pipeline has had 100% success for the past seven days.
The pipeline execution fails, and two retries that occur 15 minutes apart also fail. The third failure returns the following error.
What is a possible cause of the error?
正解:A
質問 # 127
A company uses Azure Stream Analytics to monitor devices.
The company plans to double the number of devices that are monitored.
You need to monitor a Stream Analytics job to ensure that there are enough processing resources to handle the additional load.
Which metric should you monitor?
正解:C
解説:
Explanation
There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
* Not enough processing resources in Stream Analytics to handle the volume of input events.
* Not enough throughput within the input event brokers, so they are throttled.
* Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling
質問 # 128
You develop a dataset named DBTBL1 by using Azure Databricks.
DBTBL1 contains the following columns:
SensorTypeID
GeographyRegionID
Year
Month
Day
Hour
Minute
Temperature
WindSpeed
Other
You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID. The solution must minimize storage costs.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
質問 # 129
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皆が知っているように、試験はほとんどの学生にとって難しい問題ですが、テストDP-203認定を取得し、関連する証明書を取得することは、労働者にとって非常に重要です。ただし、幸いなことに、この種の問題を心配する必要はありません。最良のソリューションであるDP-203実践教材を見つけることができるからです。当社の技術と継続的な投資と研究の補助設備により、当社の将来は明るいです。DP-203学習ツールには多くの利点があり、DP-203試験問題の合格率は99%〜100%です。 。
DP-203過去問題: https://www.it-passports.com/DP-203.html