Introduction

Azure Databricks is a cloud-based service that is used to handle big data and
analytics processing. Think of it as a turbocharged program able to turn
mountains of data into insights. This will sharpen the financial foresight of
your enterprise and improve your other products and services while growing
productivity. Picture it as an Azure Databricks notebook inside your computer
to perform and store your data analysis jobs. You will have the ability to
cleanse and process data from different sources. It will do some calculations
and visualizations to get more meaning from the data. Azure Databricks is
useful in creating and training models that can learn independently.

This article will cover some common interview questions about Azure
Databricks.

Most Asked Azure Databricks Interview Questions

1. What is Databricks?

Databricks is a company founded in
2013 in San Francisco, California. It designed Apache Spark—the foundation
one—based platform software, which is even named “Databricks.” This
open-source technology operates in the cloud and is designed for data
engineering, collaborative data science, and machine learning.

Databricks provides a collaborative environment for data engineers, data
scientists, and business analysts to work on data projects. It provides
web-based notebooks for easy development, execution, and sharing of data
analysis projects. It also provides tools to handle, transform, and prepare
data and advanced analysis such as graph processing, time series, and
geospatial analysis.

2. What do you understand by the term Azure Databricks?

The term “Azure Databricks” encapsulates the concept of a first-party PaaS
product offered by Microsoft in the Azure cloud platform. Databricks is a
web-based platform hosted on Microsoft Azure and powered by
Apache Spark, which
is implemented in Azure. It supports the creation and training of machine
learning models.

3. What are the reasons one can use Azure Databricks?

Azure Databricks is a big data processing platform and has several advantages
in terms of use, as follows:

Scalability assures alignment of the cluster resources as and when required.
This is important in managing large data sets and coping with the increasing
need for computation.

Azure Service Integration smooths work with other Azure services—Azure Blob
Storage, Azure Data Lake Storage, and Azure
SQL Database—to store, access, and analyze data.

Built on top of Apache Spark, an open-source analytics engine, Azure
Databricks allows you to use a wide variety of libraries and tools for data
processing and analytics.

4. Describe Caching?

Caching is the process of saving your most-used data in a special space so you
can access it quickly. For example, when a site is accessed many times, some
of its data is put away in the cache. When the browser loads the site from the
cache for a second look, the data is served up from the cache rather than
starting all over again from the website server, making things much quicker
and not stressing out the server in the process.

5. Is it okay to clear the cache?

Yes, it’s perfectly fine to clear the cache. The data stored in the cache is
not very important for the programs’ operation. It is there just to make
things fast and easy for you.

6. Do I need to save the results of an operation in a new variable?

You will not always need to save the results of an operation in a new
variable. This is in case you will do anything meaningful with the result. If
you need to use the result later in your project, saving it might be a good
idea.

Relevant Reading

7. Do I need to delete Data Frames that are not in use?

You would usually not want to do that unless they take up much space. If you
have a caching system in place, be careful because large data may consume much
of the available network resources.

8. How do I solve issues as and when they arrive with Azure Databricks?

The best place to start with Azure Databricks troubleshooting is their
official documentation. This has the answers to various problems and is very
helpful. Otherwise, the next best approach could be to contact Databricks
support.

9. 1Can Azure Key Vault be a replacement for Secret Scopes?

The Azure Key Vault can replace secret scopes in Azure DevOps, but it differs
completely depending on a particular need. If you must store a secret with
access across multiple Azure services and even multiple organizations, then an
Azure Key Vault might be more useful. However, secret scopes might make it
simpler to manage your secrets within one organization.

10. What programming languages are supported inside Azure Databricks?

Azure Databricks supports
Python, Scala, R, and SQL programming languages. This approach allows you to work
in whatever language makes you most comfortable or is best suited to your data
analysis needs.

11. What are some of the key features of Azure Databricks?

Some of the key features of Azure Databricks include-

Collaborative Workspaces – It offers a shared environment where data
engineers, scientists, and analysts can work together.

Data Ingestion and Preparation – Data ingestion and preparation tools
from diverse sources can be imported and prepared.

Machine Learning and AI – It offers the ability to build, deploy, and
regulate machine learning models using famous frameworks.

Advanced Analytics – One can do complex analytics like graph processing
and time series analysis.

12. What common problems will I face with Azure Databricks?

Some of the common challenges that one will face with Azure Databricks are-

Costliness – It will turn out to be costly, mainly if one is doing huge
data or clusters.

Complexity – The platform is, for the most part, complex for a newbie,
mainly if one isn’t conversant with Apache Spark.

Integration – It may require further code writing or third-party
solutions to link Azure Databricks with other tools.

Performance – It might require tuning to manage performance with
large-scale data or complex queries on the system.

Data security -Data security requires careful planning and implementing
various security measures.

13. What is the difference between an instance and a cluster in Databricks?

An instance is an Apache Spark virtual machine. A cluster is just a collection
of those instances, so you can sift through and analyze data. An instance
delivers computational power, while a cluster is just a way to combine
multiple instances to handle bigger jobs or datasets more efficiently.

14. What is the management plane in Azure Databricks?

The management plane in Azure Databricks is a set of tools and features used
to manage and configure the platform. It helps manage Spark clusters, jobs,
libraries, secrets, and configurations while ensuring that data processing is
completed in a hassle-free and efficient manner.

15. What is the control plane in Azure Databricks?

The control plane is the base platform within Azure Databricks that handles
big data-related operations. It analyzes the operations needed to run
applications optimally for Spark and ensures that the data processing tasks
are optimally performed across other service components.

To sum up, Azure Databricks is a powerful tool for working with or making
solutions with big data in the cloud. This tool is all about trying to assist
organizations in coping with, analyzing, and gaining insights from massive
data sets. This will support multiple programming languages and integrate well
with other Azure services, giving flexibility and convenience. If it’s
properly scalable and has advanced features like real-time data processing
with Kafka,
the platform could become a gem for companies hungry for big data. Knowing
Azure Databricks and answering common pitfalls will grow your data management
and analysis capabilities.

Frequently Asked Question

How do you prepare for a Databricks Interview?

Get a foundational understanding of Apache Spark because Databricks is built
on it. Concepts around big data and the need for its analysis. Code in Python,
Scala, or SQL proficiently. Look at some Databricks-specific features and
tools through their documentation and website.

What do you need to know about Databricks?

Databricks is a unified analytics platform created by the founding developers
of Apache Spark. The platform accelerates innovation by unifying data
engineering, data science, and business analytics.

How many rounds of interviews are in Databricks?

As mandated by the organization, it has approximately three to four
interviews: a phone screening, a technical round, and finally, a cultural fit
round.

To which category of cloud services does Databricks belong: SaaS, PaaS, or
IaaS?

Databricks falls under the Platform as a Service (PaaS) category. This type of
cloud computing service offers a comprehensive platform that enables users to
create, execute, and manage applications effortlessly, without dealing with
the intricacies of constructing and upkeeping the underlying infrastructure
required for app development and deployment.