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Microsoft Azure 070-475 それをもって、試験は問題になりませんよ。

あるいは、無料で試験070-475 - Design and Implement Big Data Analytics Solutions更新版問題集を更新してあげるのを選択することもできます。 NewValidDumpsはとても人気がありますから、それを選ばない理由はないです。もちろん、完璧なトレーニング資料を差し上げましたが、もしあなたに向いていないのなら無用になりますから、NewValidDumpsを利用する前に、一部の問題と解答を無料にダウンロードしてみることができます。

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070-475 PDF DEMO:

QUESTION NO: 1
You need to automate the creation of a new Microsoft Azure data factory.
What are three possible technologies that you can use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point
A. the SOAP service
B. the Microsoft .NET framework class library
C. T-SQL statements
D. Azure PowerShell cmdlets
E. the REST API
Answer: B,D,E
Explanation
https://docs.microsoft.com/en-us/azure/data-factory/data-factory-introduction

QUESTION NO: 2
You have an Apache Hive cluster in Microsoft Azure HDInsight. The cluster contains 10 million data files.
You plan to archive the data.
The data will be analyzed monthly.
You need to recommend a solution to move and store the data. The solution must minimize how long it takes to move the data and must minimize costs.
Which two services should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Azure Data Factory
B. Azure Data Lake
C. Azure Table Storage
D. Microsoft SQL Server Integration Services (SSIS)
E. Azure Queue storage
Answer: A,B
Explanation
D: To analyze data in HDInsight cluster, you can store the data either in Azure Storage, Azure Data
Lake Storage Gen 1/Azure Data Lake Storage Gen 2, or both. Both storage options enable you to safely delete HDInsight clusters that are used for computation without losing user data.
E: The Spark activity in a Data Factory pipeline executes a Spark program on your own or on-demand
HDInsight cluster. It handles data transformation and the supported transformation activities.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-use-data-lake-store
https://docs.microsoft.com/en-us/azure/data-factory/transform-data-using-spark

QUESTION NO: 3
You are designing an application that will perform real-time processing by using Microsoft
Azure Stream Analytics.
You need to identify the valid outputs of a Stream Analytics job.
What are three possible outputs that you can use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. a Hive table in Azure HDInsight
B. Azure SQL Database
C. Microsoft Power BI
D. Azure Blob storage
E. Azure Redis Cache
Answer: B,C,D
Explanation
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

QUESTION NO: 4
You use Microsoft Azure Data Factory to orchestrate data movements and data transformations within Azure.
You plan to monitor the data factory to ensure that all of the activity slices run successfully. You need to identify a solution to rerun failed slices. What should you do?
A. Move the data factory to a different resource group.
B. Delete and recreate the data factory.
C. From the Azure portal, select the Data slice blade, and then click Run.
D. From the Diagram tile on the Data Factory blade of the Azure portal, double-click the pipeline that has a failed slice.
Answer: A

QUESTION NO: 5
You need to create a query that identifies the trending topics.
How should you complete the query? 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
From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId,
SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.

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Updated: May 27, 2022