SQL questions from Capgemini data engineering interviews.
These sql questions are sourced from Capgemini data engineering interviews. Each includes an expert-level answer. This set leans toward the medium-difficulty band most real interviews actually live in (6 of 7). Recurring themes are partition, join, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 7 curated questions: 1 easy, 6 medium. 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 partition (4), join (3), spark (3), sql (2), and window (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. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. 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.
Discuss how you handled null values or unstructured data in your previous projects.
How does indexing improve query performance in SQL?
How would you deal with data skewness in a join operation?
How would you deal with data skewness in a large dataset?
Solve a problem using a window function in Spark or SQL.
map() vs mapPartitions(): Highlight the difference between map (row-level transformation) and mapPartitions (partition-level transformation).
repartition() vs coalesce(): Explain when to use repartition() (increases partitions) vs coalesce() (reduces partitions).
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