Azure Data Lake Analytics Interview Questions Nd Answers

Today’s cloud computing job market is quite competitive and getting a job is not a piece of cake. Microsoft Azure is one of the raging cloud service providers today and you must be thoroughly prepared for the interview process.

Are you preparing for Azure interview questions as you get ready to make your next cloud computing move? If so, read on for practice Azure interview questions and answers. We can’t promise you that you’ll be asked all these questions, nor can we tell you exactly what you’ll be asked, but preparing ahead of time with these will help you get mentally ready for the experience. And remember: although the cloud computing job market is booming and plenty of businesses are hiring Azure cloud architects, you will still have competition for that job. Be ready to make your best first impression by preparing for that interview.

And to help you ace your interview in one go heres a collection of 40+ Azure interview questions. To help you gear up, the Azure interview questions have been divided into the following:

Azure Data Lake Storage Interview Questions

Below are some popular interview questions on Azure Data Lake Storage.

1 What does DWU mean in Azure Synapse?

Data warehouse units are computed scale units that pool resources for I/O, memory, and CPU (DWUs). A DWU is an arbitrary, normalized indicator of computing performance and resources. The amount of DWUs available to the system changes as users change the service level, which impacts the systems efficiency and cost. You can increase/decrease the number of data warehouse units for enhanced performance. Changing data warehouse units does not affect storage costs since compute, and storage costs are billed separately.

What is an Azure Data Lake ?

In simple words Azure data lake can be described as a building a capability which can store massive amount of data (i.e. azure data lake storage), having the power and tools to transform, analyze and process data of any size (i.e. azure data lake analytics, HDInsight) included with the security provided by (Azure IAM, Azure AD).

Whole idea of Azure Data lake is to create an enterprise data solution which can store massive amounts of data and jobs can be run on it without worrying of complexities of data ingestion and storage. Azure IAM is associated with it to provide high security to the data stored and execution permission.

Which one amongst Microsoft Azure ML Studio and GCP Cloud AutoML is better?

When we compare both in terms of services, Azure ML Studio wins the verdict since it has Classification, Regression, Anomaly Detection, Clustering, Recommendation, and Ranking features.Â

On the other hand, GCP Cloud AutoML has Clustering, Regression, and Recommendation features. Moreover, Azure has a drag and drop options that make the process easier to carry out.

Which Data Factory version needs to be used to create data flows?

Using the Data Factory V2 version is recommended when creating data flows.

What are the core storage services offered by Azure?

  • Azure Blobs: An object repository for storing text and binary data.
  • Azure Files: File-sharing service run by Azure.
  • Azure Queues: It serves as a messaging service to facilitate message exchange between various modules or applications.
  • Azure Tables: NoSQL storage for storing structured data without a schema.
  • Azure Disks: Volume-level storage for blocks for Azure.
  • 1 What are the different types of storage offered by Azure?

    Storage questions are very commonly asked during an Azure Interview. Azure has four different types of storage. They are:

    Blob Storage enables users to store unstructured data that can include pictures, music, video files, etc. along with their metadata.Â

  • When an object is changed, it is verified to ensure it is of the latest version.Â
  • It provides maximum flexibility to optimize the user’s storage needs.Â
  • Unstructured data is available to customers through REST-based object storage
  • Table Storage enables users to perform deployment with semi-structured datasets and a NoSQL key-value store.Â

  • It is used to create applications requiring flexible data schema
  • It follows a strong consistency model, focusing on enterprisesÂ
  • File Storage provides file-sharing capabilities accessible by the SMB (Server Message Block) protocol

  • The data is protected by SMB 3.0 and HTTPS
  • Azure takes care of managing hardware and operating system deployments
  • It improves on-premises performance and capabilities
  • Queue Storage provides message queueing for large workloads

  • It enables users to build flexible applications and separate functions
  • It ensures the application is scalable and less prone to individual components failing
  • It enables queue monitoring which helps ensure customer demands are met
  • What is Azure Data Lake Analytics? Data Lake Explained & Examples

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