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Top PySpark Interview Questions for Your Tech Interview

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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 experienced professionals and freshers 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.

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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 to 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.

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.

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