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Azure Data Factory Online Test

Topics Covered

  • Deployment
  •  Rest API
  • Custom Activity Development
  • Migration
  •  Features
  • Data Management Gateway

Useful for hiring

  • Azure Data Developer
  • Azure Developer- Data Factory
  • Sr. Software Developer - Azure Data Factory

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Capacity Manager

I want to slice the blob based on month, year, day. I have files named as myblobcontainer/log_20151231_144229.csv which is clearly YYYYMMDD_HHMMSS.

I want to process files hourly, not re-process anything, and ideally not have to work around too much restructuring my blobs. How should I accomplish this scenario?

    • Set folder path as $$Text.Format('myblobcontainer/log_{YYYYMMDD_HHMMSS}', WindowStart)

    • Set activity source as $$Text.Format('myblobcontainer/log_{0:yyyyMMdd}', WindowStart)

    • Set folder path as $$Text.Format('myblobcontainer/log_{0:yyyyMMdd}', WindowStart)

    • Create a custom C# activity to parse the files before copying


I have recently started working on Azure Data Factory and currently exploring the JSON structures of various objects in Data Factory. I'm currently looking at the JSON structure for defining the pipeline. Which of the following properties are non-mandatory in pipeline JSON definition?

Note: There can be multiple correct answers to this question.

    • name

    • description

    • expirationTime

    • dataSets


I have created a pipeline now I want to deploy it to 3 different environments (Dev, Test, Prod). How to dynamically achieve this functionality without changing the JSON code? Which is the optimal solution?

    • Create separate custom C# utility to replace the creds and parameters as per environments

    • Create a separate version of all objects for all 3 environments

    • Create 3 configuration file in Visual Studio which will have creds and parameters respective to environments

    • Create PowerShell deploy script to replace string values in JSON dynamically

Test details:

Azure data factory skills test will evaluate a candidate's practical knowledge and will identify whether the candidate is ready to be employed. Azure data factory online test can be taken by candidates from anywhere in the comfort of their time zone.
Azure data factory pre-employment test may contain MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blank, Descriptive, Whiteboard Questions, Audio / Video Questions, True or False.

Test Duration: 20 minutes

10 Application Questions

Test description:

The Azure Data Factory service is a fully managed service for composing data storage, processing, and movement services into streamlined, scalable, and reliable data production pipelines. It allows you to create data-driven workflows to organize the movement of data between supported data stores and processing of data using computer services in other regions or in an on-premise environment. 

Azure data factory online test helps recruiters and hiring managers to assess candidate’s ability to work with Azure data factory for data integration. Azure data factory assessment test is created & validated by experienced industry experts to assess & hire Azure data developer as per the industry standards.

Online Azure Data Factory test helps to screen the candidates who possess traits as follows:

1.  Strong proficiency in Azure Data Factory, SQL Server, Azure Data Services
2.  Good knowledge of technologies, such as Azure Stream Analytics, Power BI
3.  Experience in developing solutions using Azure data factory
4.  Knowledge of creating database schemas, database models

Our powerful reporting will give you an instant result and an option to share this result with your recruiting team. You can analyze section wise performance of candidate to gauge his/her strengths and weaknesses. Features like window violation and webcam proctoring help to detect cheating during the test.

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