Real interview questions asked at HCL. Practice the most frequently asked questions and land your next role.
HCL data engineering interviews test your ability across multiple domains. These questions are sourced from real HCL interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward the medium-difficulty band most real interviews actually live in (6 of 15). Recurring themes are partition, spark, and join — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Cognizant and Nagarro, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 15 curated questions: 4 easy, 6 medium, and 5 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (9), spark (7), join (5), sql (4), optimization (3), and airflow (2). 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. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. 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 architecture are you following in your current project, and why?
What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?
How do you handle data using AWS S3?
What is your cluster configuration?
What is your data volume?
How do you sort a dictionary based on values?
What is the difference between list1 = list2 and list1.copy()?
Explain Fact Table and Star Schema.
Write a SQL query to find house with Avg(score) > 70.
Compare Spark SQL vs. Hive Performance.
Explain MapReduce Architecture.
How do you monitor Spark jobs?
What are Spark Submit properties?
Write PySpark code to extract data from a CSV and create a table.
How do you handle production deployment?
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