Real interview questions asked at TCS. Practice the most frequently asked questions and land your next role.
TCS data engineering interviews test your ability across multiple domains. These questions are sourced from real TCS interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 21 easy, 9 medium, and 14 hard questions. Recurring themes are partition, spark, and optimization — 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 44 curated questions: 21 easy, 9 medium, and 14 hard. 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 (19), spark (15), optimization (11), join (7), python (5), and sql (3). 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.
How do you handle version conflicts for libraries?
How is Azure Key Vault used to manage encryption keys in Databricks?
What are the differences between %run and dbutils.notebook.run?
Can you describe the role of user groups in setting up these policies?
How do these transformations impact memory usage?
How do you ensure version control when migrating notebooks?
How do you handle passing parameters between notebooks?
How do you identify resource bottlenecks in cluster logs?
How does cluster size impact parallelism limits?
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WAQ for Desired Output (Node Parent Relationship)
What are the implications of enabling encryption at rest on storage performance?
What are the security considerations for the control plane?
What role do workspace APIs play in this process?
What strategies do you use to retry failed steps in workflows?
Can you give an example of processing nested JSON data using these functions?
How do you install a Python library that is not in the Databricks runtime?
When would you use flatten, explode, or collect_list in Spark?
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How do these policies affect query performance?
How do you monitor and debug skewed partitions?
How does it differ from static partition pruning?
Number of Rows in Different Joins
What factors determine the optimal number of partitions for a large file?
What is dynamic partition pruning, and how does it optimize query execution?
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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