Microsoft 70-475 Online Practice Questions and Exam Preparation

Microsoft 70-475 Online Questions & Answers

• Question 121:

  You are designing a partitioning scheme for ingesting real-time data by using Kafka. Kafka and Apache Storm will be integrated.

  You plan to use four event processing servers that each run as a Kafka consumer. Each server will have a two quad-core processor.

  You need to identify the minimum number of partitions required to ensure that the load is distributed evenly.

  How many should you identify?

  A. 1
  B. 4
  C. 16
  D. 32
  C. 16

• Question 122:

  You have a Microsoft Azure Data Factory pipeline.

  You discover that the pipeline fails to execute because data is missing.

  You need to rerun the failure in the pipeline.

  Which cmdlet should you use?

  A. Set-AzureRmAutomationJob
  B. Set-AzureRmDataFactorySliceStatus
  C. Resume-AzureRmDataFactoryPipeline
  D. Resume-AzureRmAutomationJob
  B. Set-AzureRmDataFactorySliceStatus

  ---

  Use some PowerShell to inspect the ADF activity for the missing file error. Then simply set the dataset slice to either skipped or ready using the cmdlet to override the status.

  For example:

  Set-AzureRmDataFactorySliceStatus `

  -ResourceGroupName $ResourceGroup `

  -DataFactoryName $ADFName.DataFactoryName `

  -DatasetName $Dataset.OutputDatasets `

  -StartDateTime $Dataset.WindowStart `

  -EndDateTime $Dataset.WindowEnd `

  -Status "Ready" `

  -UpdateType "Individual"

  References: https://stackoverflow.com/questions/42723269/azure-data-factory-pipelines-are-failing-when-no-files-available-in-the-source

• Question 123:

  The settings used for slice processing are described in the following table.

  

  If the slice processing fails, you need to identify the number of retries that will be performed before the slice execution status changes to failed. How many retries should you identify?

  A. 2
  B. 3
  C. 5
  D. 6
  C. 5

  ---

  References: https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-factory/v1/data-factory-scheduling-and-execution.md