Easy-level spark & big data questions from real data engineering interviews.
These easy spark & big data questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview. This set leans toward fundamentals — 24 easy, 0 medium, and 0 hard questions. Recurring themes are spark, sql, and python — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 21 companies including Dunnhumby and Meesho. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 24 curated questions: 24 easy. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are spark (11), sql (9), python (6), airflow (4), etl (2), and snowflake (1). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
What is the difference between Managed and External tables in Hive/Spark?
When would you architecturally choose Dataset[T] over DataFrame in a Scala Spark pipeline, and what are the scalability and portability trade-offs? Include type-safety benefits vs. operational constraints.
What is the difference between Managed and External Tables in Databricks?
Approaches to handling multiple tasks within a sprint?
Can you share a time when you had to shift focus due to urgent tasks?
Create a DataFrame with default column types
Explain your approach to monitoring and logging Spark jobs in AWS. What tools would you use to identify performance bottlenecks?
How do you compare the time investment and value of a task?
How do you handle bad data in Databricks?
Sqoop Incremental Import?
Sqoop command for importing multiple tables
Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?
Suppose you need to import 5 tables from an external RDBMS (like MySQL) into Hadoop HDFS. Write the Sqoop command
Task Dependencies in DAG
What are the advantages of using Delta Lake over Parquet?
What are the differences between %pip and %conda commands in Databricks?
What are the different modes in which you can submit Spark jobs? Explain each.
What are the performance considerations when using Auto Loader?
What are transient clusters in EMR, and when would you use them?
Write PySpark code to filter and count records.
Write PySpark code to filter records based on specific conditions and add a calculated column.
Write a PySpark code snippet to filter rows with a specific condition.
Writing Excel sheets to Delta tables in Databricks
You are given 10 worker machines with 100 GB RAM and 25 CPU cores. How would you determine the number of executors and the size of each executor?
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