25 Most Asked Pyspark Interview Questions With Answers

This question will help the interviewer assess your basic understanding of PySpark and its role in Big Data.

Sample Answer:

“PySpark is the Python API for Apache Spark. It lets you use Python to work with big data on distributed systems. It’s great for processing large datasets quickly and supports tasks like data cleaning, analysis, and Machine Learning, all using the power of Spark under the hood.”

This question is designed to check your knowledge of extending PySpark’s capabilities using custom functions.

Sample Answer:

“A PySpark UDF, or User-Defined Function, lets me write custom logic in Python and apply it to the Spark DataFrames. It’s useful when built-in functions can’t handle our specific needs, but we must be careful because they’re slower since they break the optimised execution plan.”

The purpose behind asking this question is to understand your familiarity with SQL integration in PySpark.

Sample Answer:

“PySpark SQL allows us to run SQL queries on large datasets using Spark. It’s perfect when I want to mix SQL with Python. I can query structured data, join tables, or even register DataFrames as temporary views and run SQL queries directly on them.”

This question will test your understanding of Spark’s core data structure.

Sample Answer:

“RDD stands for Resilient Distributed Dataset. It’s Spark’s original way to store and process data across clusters. RDDs are fault-tolerant, allow parallel operations, and are great for low-level transformations. However they are less optimised than DataFrames or Datasets for most use cases today.”

This question is intended to evaluate your grasp of performance differences between PySpark and Pandas.

Sample Answer:

“Yes, PySpark is faster than Pandas when working with Big Data. Since it runs on distributed systems, it can handle massive datasets across multiple machines. Pandas is great for smaller data on a single machine, but it slows down or crashes with really large volumes.”
 

This question will help the interviewer assess your knowledge of the entry point to PySpark applications.

Sample Answer:

“SparkSession is the main entry point in PySpark. It lets me create DataFrames, run SQL queries, and manage resources. It combines SQLContext and HiveContext into one object, making it easier to work with data. We can’t run anything in PySpark without creating a SparkSession first.”

This question will test your understanding of PySpark’s connection to the cluster.

Sample Answer:

“SparkContext is the engine that connects my PySpark application to the Spark Cluster. It sets up the execution environment, manages resources, and coordinates distributed data processing. Although SparkSession is used more now, SparkContext still runs in the background to make everything work.”

This question is intended to check if you understand how PySpark handles data conversion.

Sample Answer:

“Serializers in PySpark convert objects into a format that can be transferred over the network or stored. PySpark supports PickleSerializer and MarshalSerializer. Using the right serializer can improve performance, especially when shuffling data across nodes in the cluster.”

This question will help your interviewer evaluate your understanding of how to share files across executors.

Sample Answer:

“PySpark’s SparkFiles lets us send extra files like configs or scripts to all worker nodes in the cluster. This is handy when my job depends on external files. I use SparkContext.addFile() to distribute the file and SparkFiles.get() to access it on the workers.”

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Your answer to this question will assess your knowledge of performance monitoring in PySpark.

Sample Answer:

“Profilers in PySpark help track the performance of my jobs. They show which parts of the code take the most time or resources. PySpark includes a BasicProfiler by default, but I can use custom profilers to dig deeper into bottlenecks and improve efficiency.”
 

This question is designed to evaluate your practical knowledge of writing and registering custom functions.

Sample Answer:

“Creating a UDF is simple! I define a Python function, then register it with F.udf() from pyspark.sql.functions. After that, I can use it on DataFrames just like any other function. Then I specify the return type as it helps PySpark handle the data properly.”

This question is intended to check your understanding of configuring Spark applications.

Sample Answer:

“SparkConf is how I set up configuration settings for my Spark application. I can use it to define the app name, master URL, memory size, and more. It’s the first step in tuning performance and behaviour before my app even starts running.”
 

This question is posed to evaluate your knowledge of efficient data sharing across nodes.

Sample Answer:

“Broadcast variables let me send read-only data to all worker nodes without copying it repeatedly. For example, if I’m joining a big DataFrame with a small lookup table, broadcasting the smaller one saves memory and speeds things up. It’s ideal for reference data that doesn’t change.”
 

This question will help the interviewer confirm your understanding of custom functions in DataFrame operations.

Sample Answer:

A UDF, or User-Defined Function, is a custom Python function I can apply to Spark DataFrames. I use it when built-in functions aren’t enough. After registering it, I can apply it in a withColumn() call or SQL query. It's important to remember that they’re slower than native functions.

This question will test how good your grasp of machine learning workflows in Spark is.

Sample Answer:

“A pipeline in PySpark’s MLlib lets me chain together multiple steps like data transformation and model training. It’s useful because it makes my workflow reusable and easy to tune. I can set stages in order, and Spark handles everything, from data prep to prediction.”

This question will help the interviewer gauge your knowledge of fault tolerance in PySpark.

Sample Answer:

“A checkpoint in PySpark saves the state of RDDs or DataFrames to stable storage like HDFS. It helps when lineage graphs get too long or complex. If something fails, Spark can restart from the checkpoint instead of recomputing everything; great for stability and recovery.”

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This question is designed to assess your understanding of data reuse in memory.

Sample Answer:

“Caching stores DataFrames or RDDs in memory, so Spark doesn’t recompute them every time they’re used. It’s a huge performance boost, especially if I’m using the same data in multiple stages. Just call .cache() on the dataset before running my actions.”
 

The intent behind asking this question is to test your knowledge of advanced SQL-style analytics.

Sample Answer:

“A window function performs calculations across a sliding range of rows, like running totals or rankings, without collapsing the data. In PySpark, I use Window to define the partition and order, then apply functions like row_number() or avg(). It’s perfect for grouped, row-wise comparisons.”
 

The purpose of this question is to see if you understand functional transformations in RDDs.

Sample Answer:

“map() applies a function to each element and returns one result per input. flatMap() does the same but flattens the results if each input returns multiple outputs. So, flatMap() is great when I need to break down data into smaller pieces.
 

This question will assess your ability to extend PySpark with tailored logic.

Sample Answer:

“To create a custom transformation, define a function that takes and returns a DataFrame. Inside, use PySpark operations like withColumn() or filter(). Then I can reuse it across workflows by just calling the function, which is very handy for keeping my code modular and clean.”

Your answer to this question will help assess your understanding of join optimisation in PySpark.

Sample Answer:

“A broadcast join sends a small table to all worker nodes so it doesn’t have to be shuffled. It’s faster than standard joins, which move large chunks of data across the cluster. Use broadcast joins when one table is small enough to fit in memory.”

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With this question, the interviewer seeks to test your knowledge of Resource Management in Spark.

Sample Answer:

“Spark supports several cluster Managers: Standalone, YARN, Mesos, and Kubernetes. Each manages resources and job scheduling differently. YARN is popular with Hadoop, Kubernetes is great for containerised setups, and Standalone is simple for smaller deployments. I choose based on my infrastructure and needs.”

This question is designed to check your ability to initialise Spark and use it effectively.

Sample Answer:

“I create a SparkSession with SparkSession.builder.appName("YourApp").getOrCreate(). It’s the gateway to everything in PySpark, creating DataFrames, running SQL, reading data, and managing resources. Without it, we can’t interact with Spark’s core features in a PySpark program.”

This question will help evaluate your knowledge of data distribution.

Sample Answer:

“Partitioning splits data into chunks across the cluster, enabling parallel processing. More partitions can boost performance if managed well. I can control partitions manually using repartition() or coalesce(), especially after filtering or joining. Good partitioning reduces shuffling and improves speed.”

The intent behind this question is to confirm your understanding of memory-based optimisation.

Sample Answer:

“Caching is done using .cache() or .persist() on a DataFrame or RDD. It keeps the data in memory for reuse, saving time on repeated operations. This is especially useful in iterative processes or when the same data is used in multiple actions.”