8 PySpark Interview Questions (With Example Answers)

Updated 8 September 2022

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As technology becomes increasingly prevalent across industries, many companies hire developers to help them design products. An application programming interface (API), such as PySpark, is a software interface that connects two or more designated software programs to each other and allows them to communicate. If you want a job as a PySpark developer or in a related field, such as data engineering, reviewing some common interview questions can help you prepare. In this article, we list some common PySpark interview questions, explain why interviewers ask those questions and provide example answers to help you prepare responses for your own interview.

PySpark Interview Questions With Example Answers

If you are applying for a Python, data engineering, data analyst or data science job, practising PySpark interview questions is essential because employers frequently want you to be familiar with robust data-processing tools and frameworks, such as PySpark.

Interview questions regarding PySpark are usually technical in nature. Employers ask these questions to understand your knowledge of functions and processes for data. Hiring managers may ask in-depth questions about PySpark to test your technical expertise. Interviewers might also ask you about your professional experience and education to gauge your suitability for the position. Here are a few example questions, along with their answers, to help you prepare for your interview:

1. Explain what PySpark is and how to use it.

The hiring manager might ask you this interview question to gauge whether you possess a basic understanding of the subject. In your answer, explain what PySpark is and provide a brief explanation of its applications in your response.

Example answer: 'The Apache Spark community and Python collaborated to create PySpark, an interface for Apache Spark that allows Python to operate with Spark. This tool works with Apache Spark utilising Python API to enable features like Spark SQL, Spark DataFrame, Spark Streaming, Spark Core and Spark MLlib. By providing efficient APIs that enable the application to read data from many information sources, it offers an interactive PySpark shell to analyse and process structured and semi-structured data in a distributed context.'

2. What is PySpark SparkContext?

Hiring managers may ask you this question to understand whether you possess in-depth knowledge of PySpark. In your answer, you may briefly explain the functions it serves.

Example answer: 'PySpark SparkContext serves as the primary point of entry for the Spark functionalities. Once the Spark application launches, it starts the driver program and initiates SparkContext along with the main function. The driver application then executes the tasks inside the worker nodes' executors. It starts a Java virtual machine using the Py4J library before creating a JavaSparkContext. It does not require building a new SparkContext because PySpark's SparkContext is already available as 'sc'. Additionally, we can utilise it to build Spark RDDs and broadcast variables across the cluster.'

3. What do you mean by PySpark architecture?

Hiring managers may use this question to gauge the skill level of experienced candidates and judge their suitability for the role. In your response, try to explain what the term means concisely.

Example answer: 'PySpark uses a paradigm in which one element controls another, just like Apache Spark. Here, the controlling node is the driver, and the others are worker nodes. The Spark Driver builds a SparkContext during the execution of the application, which serves as the entry point. The worker nodes handle all the operations. Cluster managers administer the resources necessary for the operations that affect the worker nodes.'

4. What can you tell us about RDD in PySpark?

Hiring managers might ask this question to determine your understanding of resilient distributed dataset (RDD). Explain what the term means in regard to PySpark in your answer. You can also consider mentioning some features of RDD to demonstrate your knowledge even further.

Example answer: 'RDD is an acronym for resilient distributed datasets in PySpark. It is a fundamental PySpark data structure. It is a low-level object that handles distributed jobs very effectively. PySpark RDDs are components that can execute and function on several nodes to provide parallel processing on a cluster. These are unchangeable or immutable components. This means that once you create an RDD, you cannot change it. RDDs can withstand faults as well. In the event of any failure, they automatically recover. We can use a variety of operations on RDDs to accomplish a specific objective.'

5. How do you implement machine learning in PySpark?

Hiring managers may wish to know if you possess the level of competence that allows you to use PySpark in the field of machine learning. Consider preparing a brief explanation of how PySpark helps to execute machine learning.

Example answer: 'MLlib is a tool that allows us to integrate machine learning into Spark. PySpark offers machine learning with the help of its MLlib record, which is scalable. We can use it to develop machine learning that is adaptable and simple using common learning algorithms and use cases like clustering, weakening filtering and dimensional lessening.'

6. Please explain what Spark core is, along with the key functions of Spark core.

This question lets your interviewers know about your expertise on the Spark platform and its features. When answering this question, provide the necessary information that allows them to determine your knowledge of the subject. Try to keep your response brief and relevant.

Example answer: 'SparkCore is a general execution engine that supports all the functions of the Spark platform. It includes Java, Scala and Python APIs that simplify development, in-memory processing capabilities to deliver a better speed, and a generalised execution paradigm to accommodate a variety of applications. The primary functions of SparkCore include all fundamental input and output (I/O) operations, storage scheduling and monitoring. Additionally, it is in charge of efficient memory management and fault recovery.'

7. What is a PySpark partition? What are its uses?

Hiring managers may want to test your knowledge regarding partitions in PySpark by asking you this question. This also helps them learn about your skills in the PySpark API. Consider explaining what you know about partitions in your answer briefly.

Example answer: 'PySpark partition is a method for dividing a sizable dataset into smaller ones using one or more partition keys. Due to the concurrent execution of each partition's transformations, transformations on partitioned data run more quickly. PySpark provides support for both partitioning in memory and partitioning on a disc. When creating a DataFrame from a file or table, PySpark divides the data into a certain number of divisions according to predetermined criteria. It also makes it easier to create a partition in several columns.'

8. How does PySpark serve different industries in the workforce?

By asking this interview question, hiring managers may wish to determine if you are aware of the scope and versatility of PySpark. Consider offering a few examples of one or two industries where PySpark can prove beneficial and explain how.

Example answer: 'Real-time media streaming, financial analysis, e-commerce recommendations and telecommunication services are just a few of PySpark's industrial applications. For example, healthcare providers can use Spark to analyse the patient's prior medical records to determine the health difficulties patients may experience after discharge. They might also utilise Spark to undertake genome sequencing to speed up the processing of genome data. Travel companies may employ Spark to compare information and reviews from different websites about the location, hotels and other travel-related topics to assist customers plan the ideal vacation and offer personalised recommendations to travellers.'

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