Spark & Big Data questions from TCS data engineering interviews.
These spark & big data questions are sourced from TCS data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (12 of 19 are tagged hard). Recurring themes are partition, optimization, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Meesho, 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 19 curated questions: 5 easy, 2 medium, and 12 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 (12), optimization (10), spark (6), sql (3), join (2), and python (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.
Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.
Can you give a use case where Delta Live Tables would be ideal?
Explain Delta Live Tables and their features, such as declarative pipeline definition and automatic data validation.
Explain data encryption in Databricks, both at rest and in transit.
Explain the architecture of Databricks, including the control plane and data plane.
How do Delta Live Tables ensure data quality during transformations?
How do you implement row and column-level security in Databricks?
How do you move a Databricks notebook to higher environments?
How does Auto Loader avoid reloading files with the same name?
How does Databricks integrate with external storage systems?
How would you read a large file (e.g., 15GB) efficiently in Spark by increasing parallelism?
What are the differences between %pip and %conda commands in Databricks?
What are the performance considerations when using Auto Loader?
What are the steps to debug a failed workflow in Databricks?
What determines the maximum parallelism achievable in Databricks?
What happens if the checkpoint location is accidentally deleted?
What is Databricks Auto Loader, and how does it handle new files?
What is the importance of the checkpoint location in Databricks?
What role does executor memory and CPU configuration play in maximizing parallelism?
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