The Ultimate PySpark Practice Questions

What Challenges Will This Module Solve?

Mastering PySpark isn’t just about learning syntax — it’s about applying it to real-world data problems. Many engineers struggle with inefficient transformations, slow queries, and debugging distributed workloads. This module is designed to help you write optimized PySpark code, handle large datasets efficiently, and troubleshoot common issues.

Key challenges сovered:

• Performance optimization – Learn how to minimize shuffling, optimize joins, and speed up queries.
• Efficient data processing – Understand when to use RDDs, DataFrames, and Spark SQL for best performance.
• Debugging & troubleshooting – Identify bottlenecks, fix memory errors, and improve execution plans.
• Real-world problem solving – Work on hands-on exercises that mirror actual data engineering tasks.
• Interview preparation – Practice PySpark coding challenges commonly asked in job interviews.

By the end of this module, you’ll know how to process big data efficiently, optimize Spark jobs, and confidently tackle PySpark challenges in both production environments and technical interviews.

What You’ll Learn

his module covers all the key aspects of PySpark development, guiding you through a series of practice questions focused on Spark’s core functionalities.

 PySpark architecture & SparkSession API

• Learn how Spark clusters distribute computations and manage data processing.
• Understand the role of the SparkSession as the entry point for PySpark applications.
• Configure and optimize SparkSession parameters for better performance.

RDD API – the foundation of PySpark

• Understand how Resilient Distributed Datasets (RDDs) work and when to use them.
• Learn transformations and actions, including map, filter, reduce, and flatMap.
• Optimize RDD operations to minimize shuffling and improve performance.

DataFrame API – high-level data manipulation

• Work with structured data using DataFrames, similar to SQL tables.
• Use functions like select, filter, orderBy, and distinct for efficient data processing.
• Understand how DataFrame operations differ from RDDs and when to use each.

Read, write, and save API – data ingestion and storage

• Load data from CSV, Parquet, JSON, and databases.
• Understand data serialization formats and how they impact performance.
• Learn how to save transformed data efficiently for further processing.

Filtering, collecting, and taking data

• Use the where() and filter() functions to extract relevant data efficiently.
• Collect and take small datasets for quick exploration without overloading memory.
• Learn the performance implications of using collect() improperly.

Handling missing data & duplicates

• Drop or fill missing values using dropna() and fillna().
• Deduplicate datasets efficiently using distinct() and dropDuplicates().
• Learn how improper handling of missing data can impact analytical accuracy.

Column operations & transformations

• Modify existing columns using withColumn() and withColumnRenamed().
• Perform complex column transformations like case conversion and type casting.
• Use UDFs (User-Defined Functions) for advanced column operations.

Sorting, ordering, and aggregation

• Understand how orderBy() and sort() work in distributed processing.
• Group and aggregate data using groupBy(), agg(), and count().
• Learn efficient aggregation strategies for large datasets.

Joins and unions – combining data

• Master different join types: inner, left, right, and outer joins.
• Optimize join performance using broadcast joins and avoiding data skew.
• Use union() to merge multiple DataFrames efficiently.

Partitioning, repartitioning, and coalescing

• Learn how partitions impact PySpark performance.
• Understand when to use repartition() vs. coalesce() for optimal resource utilization.
• Avoid common mistakes that lead to data skew and inefficient shuffling.

By the end of this module, you’ll not only understand PySpark but also know how to use it effectively for building scalable, production-grade data pipelines.

Tools and Technologies 

PySpark in real-world data engineering environments. You’ll gain practical experience with:

• Apache Spark – The backbone of distributed data processing.
• PySpark APIs – Leveraging RDDs, DataFrames, and Spark SQL for efficient data manipulation.
• SparkSession API – Managing Spark applications and optimizing performance.
• Data Formats – Handling CSV, JSON, Parquet, and ORC files seamlessly.
• Performance Optimization – Implementing partitioning, caching, and shuffling strategies to enhance efficiency.
• ETL Pipeline Design – Applying PySpark techniques to build scalable data workflows.

By working through this module, you’ll develop real-world expertise in processing, transforming, and optimizing big data workloads effectively.

Real stories of student success

▶

Student TRIPLES Salary with Data Engineer Academy

▶

DEA Testimonial – A Client’s Success Story at Data Engineer Academy