8 PySpark Interview Questions and How to Answer Them

As technology use explodes across industries, many companies seek developers who can help them expand their operations and create better customer experiences. Many companies use application programming interfaces (APIs) to design products like PySpark. These interfaces connect software programs and enable them to communicate. 

When seeking a position using PySpark, understanding the interface and how to answer common interview questions can help you stand out to employers. Technical interviews allow employers to get to know you, understand your skill set, and see if you are a good fit for the position. These interviews typically focus on more specific knowledge, as employers want to feel confident you can perform the role's responsibilities. 

This article will discuss the types of questions you can expect in a PySpark interview and offer tips for acing them.

8 common PySpark interview questions

When entering a PySpark-focused technical interview, your interviewer will likely ask questions about your knowledge of PySpark and its applications. Consider preparing answers to the following questions.

1. What is PySpark?

Why they’re asking: When hiring a professional to work with PySpark, hiring managers may ask about your language knowledge to ensure you are up for the job. You will want to give a basic overview of your understanding of the interface in your answer.

How to answer: PySpark is the Python API for Apache Spark. This interface allows professionals to write Spark applications using Python APIs and the PySpark shell to analyse data interactively. PySpark also allows using several of Spark’s features, including Spark SQL, DataFrame, streaming, machine learning, and Spark Core. PySpark is commonly used with these tools in data science and machine learning applications. 

Other forms this question may take:

• What sets PySpark apart from other interfaces?

• Can you explain the key features of PySpark?

2. How do you use machine learning in PySpark?

Why they’re asking: As the use of machine learning expands, hiring managers may ask whether you can use PySpark to implement this type of technology within their business. While you may not need in-depth experience with machine learning with PySpark, a basic understanding can help you stand out.

How to answer: MLlib is a Spark tool you can use with PySpark to integrate machine learning functions. PySpark can scale machine learning algorithms with MLlib, and you can use PySpark to develop algorithms and find trends within the data sets.

Other forms this question may take:

• What is your experience with machine learning?

• What use does machine learning have in PySpark?

3. What is PySpark architecture?

Why they’re asking: How you describe the architecture of PySpark may signal to hiring managers how deeply you understand APIs and your level of skill and experience in this field. Depending on the level of your role, hiring managers may be looking for different skill levels.

How to answer: PySpark is an open-source, distributed framework and library set that allows for quick processing of extremely large data sets. It uses a paradigm similar to Apache Spark, where the controlling node drives the operations, and worker nodes follow. This in-memory cluster computing framework uses implicit data parallelism and fault tolerance. 

Other forms this question may take:

• Can you describe PySpark's architecture?

• Can you explain how PySpark architecture sets it apart?

4. What is SparkContext?

Why they’re asking: SparkContext is the basis of all spark functions. This is important knowledge for any PySpark developer, and hiring managers may ask about your knowledge of this function.

How to answer: SparkContext is the gateway to spark functionality. When opening and running the Spark application, you initiate SparkContext to run and execute any operation inside the interface. 

Other forms this question may take:

• Can you explain a time you used SparkContext?

• Why is SparkContext important?

5. What do you know about RDD?

Why they’re asking: Resilient distributed datasets (RDD) are important API components. A hiring manager may want to ensure you have a strong working knowledge of RDDs and can complete basic abstraction. In your answer, you will want to demonstrate you clearly understand how to use RDDs and how this applies to PySpark applications.

How to answer: An RDD is a collection of resilient elements. This type of dataset is unchangeable and immutable, which means it will automatically recover if there is a system failure. Computers store RDDs on memory or disk space on different machines within a cluster, and each separately stored part is a partition. In PySpark, RDDs are low-level objects that are effective for handling distributed jobs and completing parallel processing on a cluster by executing functions of several nodes at a time. 

Other forms this question may take:

• What is a resilient distributed data set?

• Can you describe how RDDs are used?

6. How does PySpark use partitions?

Why they’re asking: Partitions are useful for storing and managing data within PySpark. Hiring managers may ask about your knowledge of their use and function.

How to answer: Using partitions in PySpark allows you to store large data sets into smaller segments using predetermined criteria for the divisions. Partitions can be stored either in memory or on a disk. This can improve the performance of the data set by creating smaller file systems to store and structure the data. 

Other forms this question may take:

• What are partitions in PySpark?

• How would you store large data sets in smaller segments?

7. What are the applications of PySpark?

Why they’re asking: Depending on the job position, your hiring manager may ask how you view potential PySpark applications. Showing knowledge of how you can use PySpark across industries can indicate that you are a creative problem solver with the potential to think of novel solutions.

How to answer: PySpark can be used with extremely large datasets to find patterns and trends quickly. Because of this, it can be used effectively in several industries, and we have seen successes in health, finance, entertainment, e-commerce, and education. PySpark has been used to analyse patient medical records, sequence genomes, compare products and services, and create personalised recommendations. 

Other forms this question may take:

• What applications of PySpark would be appropriate for this position?

• What types of problems do you anticipate solving using PySpark?

8. What is a cluster manager, and what types are available in PySpark?

Why they’re asking: A cluster manager is the mode platform that allows Spark to run. When working with PySpark, managing and understanding cluster manager ecosystems is key to ensuring worker nodes have the necessary memory, processor allocation, and requirements met.

How to answer: A cluster manager is the key platform for Spark, providing worker nodes with the appropriate structure and information to execute functions. In PySpark, cluster management types that are commonly used include Standalone, Apache Mesos, Hadoop YARN, local, and Kubernetes.

Other forms this question may take:

• Why would you use a cluster manager?

• What cluster manager types have you used and why?

Tips to prepare for your interview

Entering a technical interview can feel intimidating, but remember to be confident in your knowledge and preparation. Interviews are an exciting opportunity to learn more about the position, interact with potential colleagues, and showcase why you are a great fit for the role. When preparing for an interview, keep these tips in mind:

• Stay calm. Think carefully about each question and be honest in your answers. If you are unsure what the interview question means, ask for clarification. 

• Reflect on your personal and professional strengths. Why are you applying for this position? What do you expect to bring to the table? If you are clear about why this is the right role for you, you will better be able to relay this to hiring managers.

• Research the company and the role. A working knowledge of the role you are applying for and your responsibilities within the company can help prepare you to answer questions.

• Have questions ready. Having carefully thought out questions for your interviewer can show your dedication to the role and excitement about the position.

Next steps

Building a strong basis in PySpark can prepare you to enter roles in this industry. Consider completing Guided Projects on Coursera, such as Graduate Admission Prediction with PySpark ML or a course like Python and Pandas for Data Engineering, to build job-ready skills and gain real-world experience.