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Top PySpark Interview Questions for Your Tech Interview

by Interview Kickstart Team in Interview Questions
October 10, 2024
Want to know what it takes to nail data interviews at FAANG?

Top PySpark Interview Questions for Your Tech Interview

Last updated by Utkarsh Sahu on Oct 04, 2024 at 04:15 PM | Reading time: 7 minutes

You can download a PDF version of  
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PySpark interview questions are typically asked at data interviews where companies evaluate candidates on their knowledge of big data tools and frameworks. PySpark is essentially an open-source Python API for Apache Spark. It is a distributed computing framework containing a set of libraries, used widely for large-scale and real-time data processing of data.

We’ve listed these common PySpark interview questions for freshers and experienced professionals to give you an idea of what to expect at your interview. Knowing how to use the framework is a big bonus in data and software engineering interviews and can tile the odds in your favor.

If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready!

Having trained over 11,000 software engineers, we know what it takes to crack the most challenging tech interviews. Our alums consistently land offers from FAANG+ companies. The highest ever offer received by an IK alum is a whopping $1.267 Million!

At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies.

Want to nail your next tech interview? Sign up for our FREE Webinar

Let’s go ahead and look at some common Panda interview questions asked in technical interviews.

Let’s go ahead and look at some common PySpark interview questions for freshers and experienced professionals. We’ll also look at some frequently asked questions around the Python framework.

Here’s what we’ll cover in this article:

  • Benefits of Using PySpark
  • PySpark Interview Questions for Freshers
  • PySpark Interview Questions for Experienced Developers
  • FAQs on PySpark Interview Questions

Benefits of Using PySpark

PySpark is a big data framework tool for Python that helps to perform large-scale data processing, analysis, and manipulation. The tool offers many benefits to developers and data professionals, as it is equipped with an extensive library with functions capable of performing a wide range of tasks.

Some notable benefits of using PySpark include:

  1. PySpark supports In-memory processing, a feature that rapidly improves the speed of processing large data sets
  2. PySpark has over 80 operators, helping data analysts perform a wide range of data-related tasks
  3. PySpark is dynamic and offers third-party integration
  4. Many essential algorithms are integrated within the framework, offering users the ease to carry out complex tasks
  5. Programmers and data professionals can easily handle and manage errors
  6. PySpark's huge set of libraries makes it possible for developers to perform complex data processing operations

PySpark Interview Questions for Freshers

If you’re a fresher applying for a Python developer or data analyst role, being able to answer common PySpark interview questions is essential from an interview perspective.

To give you an idea of the type of questions asked, below are some common PySpark interview questions.

  1. What are the main characteristics of the PySpark framework?
  2. What is SparkConf in PySpark?
  3. What do you understand about SparkFiles in PySpark?
  4. How do you get the absolute path of a file in PySpark?
  5. Which function is used to retrieve the root directory in PySpark?
  6. Explain the different storage levels in PySpark.
  7. What are broadcast variables in PySpark?
  8. What is Martial and Pickel Serializers in the PySpark framework?
  9. How do you get information about Spark Jobs in PySpark?
  10. What are filters in PySpark?
  11. Explain Accumulator variables in PySpark.
  12. Explain the SparkStageInfo in PySpark.

PySpark Interview Questions for Experienced Professionals

Experienced Python programmers, developers, data engineers, data scientists, and analysts are expected to know their way around PySpark. If you’re an experienced tech professional, you can expect quite a few PySpark interview questions for experienced professionals at tech interviews.

Here are the top questions to give you context about the questions to expect.

  1. Which are some algorithms supported and integrated into PySpark?
  2. How do you create a UDF in PySpark?
  3. What do you understand about the PySpark DAGScheduler?
  4. What do you understand by shared variables in PySpark?
  5. What are DataFrames in PySpark? How are they different from DataFrames in Pandas?
  6. How do you create a SparkSession in PySpark?
  7. How do you create a Resilient Distributed DataSet in PySpark? What are the approaches you can take?
  8. How do you join two DataFrames in PySpark?
  9. How do you go about performing streaming in PySpark?
  10. What do you understand about profilers in PySpark?

Practicing these above PySpark interview questions can help you prepare for your upcoming technical interview. For specific coding-related problems for technical interviews, check out our Problems Page.  

Gear Up for Your Next Technical Interview

If you want to crack your next software developer interview, register for our Full Stack Masterclass to find out how we can help you. Interview Kickstart is the gold standard in tech interview prep.

Our programs include a comprehensive curriculum, unmatched teaching methods, FAANG+ instructors, and career coaching to help you nail your next tech interview.

Check out some reviews from our students to understand how we’ve shaped the careers of thousands of engineers by helping them land high-paying offers from the biggest tech companies.

FAQs on PySpark Interview Questions

Q1. What is PySpark?

PySpark is an open-source, cluster-computing framework integrated into Apache Spark. It helps to perform large-scale data analysis and processing by offering a wide set of libraries that help developers and analysts to perform complex data-related functions.  

Q2. Why is it important to practice PySpark interview questions?

Practicing PySpark interview questions is crucial if you’re appearing for a Python, data engineering, data analyst, or data science interview, as companies often expect you to know your way around powerful data-processing tools and frameworks (like PySpark).

Q3. What roles require a good understanding and knowledge of PySpark?

Roles that require a good understanding of PySpark include - data engineering, data scientist, data analyst, big-data, and Python developer.

Q4. What are some benefits of PySpark?

Some benefits of PySpark are - i) it offers a huge set of libraries, ii) it can be integrated with 3rd party applications, iii) it allows for swift data processing, up to 10x faster than traditional frameworks iv) it is easy to use.

Q5. What are some industrial benefits of using PySpark?

Some industrial applications of PySpark include - Real-time media streaming, Financial analysis, processing healthcare information and analyzing healthcare records, in-app travel and e-commerce recommendations, and telecommunication services.



Related Reads: 

1. 100+ AWS Interview Questions for Tech Interview Preparation

2. Top iOS Interview Questions and Answers You Should Prepare

3. Java OOPs Interview Questions to Crack Coding Interviews

4. Database Management System (DBMS) MCQs for IT Specialists

5. Artificial Narrow Intelligence: Examples, Challenges, and Types

Author
Utkarsh Sahu
Director, Category Management @ Interview Kickstart || IIM Bangalore || NITW.
The fast well prepared banner

PySpark interview questions are typically asked at data interviews where companies evaluate candidates on their knowledge of big data tools and frameworks. PySpark is essentially an open-source Python API for Apache Spark. It is a distributed computing framework containing a set of libraries, used widely for large-scale and real-time data processing of data.

We’ve listed these common PySpark interview questions for freshers and experienced professionals to give you an idea of what to expect at your interview. Knowing how to use the framework is a big bonus in data and software engineering interviews and can tile the odds in your favor.

If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready!

Having trained over 11,000 software engineers, we know what it takes to crack the most challenging tech interviews. Our alums consistently land offers from FAANG+ companies. The highest ever offer received by an IK alum is a whopping $1.267 Million!

At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies.

Want to nail your next tech interview? Sign up for our FREE Webinar

Let’s go ahead and look at some common Panda interview questions asked in technical interviews.

Let’s go ahead and look at some common PySpark interview questions for freshers and experienced professionals. We’ll also look at some frequently asked questions around the Python framework.

Here’s what we’ll cover in this article:

  • Benefits of Using PySpark
  • PySpark Interview Questions for Freshers
  • PySpark Interview Questions for Experienced Developers
  • FAQs on PySpark Interview Questions

Benefits of Using PySpark

PySpark is a big data framework tool for Python that helps to perform large-scale data processing, analysis, and manipulation. The tool offers many benefits to developers and data professionals, as it is equipped with an extensive library with functions capable of performing a wide range of tasks.

Some notable benefits of using PySpark include:

  1. PySpark supports In-memory processing, a feature that rapidly improves the speed of processing large data sets
  2. PySpark has over 80 operators, helping data analysts perform a wide range of data-related tasks
  3. PySpark is dynamic and offers third-party integration
  4. Many essential algorithms are integrated within the framework, offering users the ease to carry out complex tasks
  5. Programmers and data professionals can easily handle and manage errors
  6. PySpark's huge set of libraries makes it possible for developers to perform complex data processing operations

PySpark Interview Questions for Freshers

If you’re a fresher applying for a Python developer or data analyst role, being able to answer common PySpark interview questions is essential from an interview perspective.

To give you an idea of the type of questions asked, below are some common PySpark interview questions.

  1. What are the main characteristics of the PySpark framework?
  2. What is SparkConf in PySpark?
  3. What do you understand about SparkFiles in PySpark?
  4. How do you get the absolute path of a file in PySpark?
  5. Which function is used to retrieve the root directory in PySpark?
  6. Explain the different storage levels in PySpark.
  7. What are broadcast variables in PySpark?
  8. What is Martial and Pickel Serializers in the PySpark framework?
  9. How do you get information about Spark Jobs in PySpark?
  10. What are filters in PySpark?
  11. Explain Accumulator variables in PySpark.
  12. Explain the SparkStageInfo in PySpark.

PySpark Interview Questions for Experienced Professionals

Experienced Python programmers, developers, data engineers, data scientists, and analysts are expected to know their way around PySpark. If you’re an experienced tech professional, you can expect quite a few PySpark interview questions for experienced professionals at tech interviews.

Here are the top questions to give you context about the questions to expect.

  1. Which are some algorithms supported and integrated into PySpark?
  2. How do you create a UDF in PySpark?
  3. What do you understand about the PySpark DAGScheduler?
  4. What do you understand by shared variables in PySpark?
  5. What are DataFrames in PySpark? How are they different from DataFrames in Pandas?
  6. How do you create a SparkSession in PySpark?
  7. How do you create a Resilient Distributed DataSet in PySpark? What are the approaches you can take?
  8. How do you join two DataFrames in PySpark?
  9. How do you go about performing streaming in PySpark?
  10. What do you understand about profilers in PySpark?

Practicing these above PySpark interview questions can help you prepare for your upcoming technical interview. For specific coding-related problems for technical interviews, check out our Problems Page.  

Gear Up for Your Next Technical Interview

If you want to crack your next software developer interview, register for our Full Stack Masterclass to find out how we can help you. Interview Kickstart is the gold standard in tech interview prep.

Our programs include a comprehensive curriculum, unmatched teaching methods, FAANG+ instructors, and career coaching to help you nail your next tech interview.

Check out some reviews from our students to understand how we’ve shaped the careers of thousands of engineers by helping them land high-paying offers from the biggest tech companies.

FAQs on PySpark Interview Questions

Q1. What is PySpark?

PySpark is an open-source, cluster-computing framework integrated into Apache Spark. It helps to perform large-scale data analysis and processing by offering a wide set of libraries that help developers and analysts to perform complex data-related functions.  

Q2. Why is it important to practice PySpark interview questions?

Practicing PySpark interview questions is crucial if you’re appearing for a Python, data engineering, data analyst, or data science interview, as companies often expect you to know your way around powerful data-processing tools and frameworks (like PySpark).

Q3. What roles require a good understanding and knowledge of PySpark?

Roles that require a good understanding of PySpark include - data engineering, data scientist, data analyst, big-data, and Python developer.

Q4. What are some benefits of PySpark?

Some benefits of PySpark are - i) it offers a huge set of libraries, ii) it can be integrated with 3rd party applications, iii) it allows for swift data processing, up to 10x faster than traditional frameworks iv) it is easy to use.

Q5. What are some industrial benefits of using PySpark?

Some industrial applications of PySpark include - Real-time media streaming, Financial analysis, processing healthcare information and analyzing healthcare records, in-app travel and e-commerce recommendations, and telecommunication services.



Related Reads: 

1. 100+ AWS Interview Questions for Tech Interview Preparation

2. Top iOS Interview Questions and Answers You Should Prepare

3. Java OOPs Interview Questions to Crack Coding Interviews

4. Database Management System (DBMS) MCQs for IT Specialists

5. Artificial Narrow Intelligence: Examples, Challenges, and Types

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Register for Webinar

Recession-proof your Career

Recession-proof your Software Engineering Career

Attend our free webinar to amp up your career and get the salary you deserve.

Ryan-image
Hosted By
Ryan Valles
Founder, Interview Kickstart
blue tick
Accelerate your Interview prep with Tier-1 tech instructors
blue tick
360° courses that have helped 14,000+ tech professionals
blue tick
57% average salary hike received by alums in 2022
blue tick
100% money-back guarantee*
Register for Webinar

Attend our Free Webinar on How to Nail Your Next Technical Interview

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Select webinar slot
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