This blog is based on actual questions asked in a TCS Data Engineer Interview (Round 1) and, more importantly, explains what interviewers really expect behind each question. If you are preparing for TCS or similar data engineering roles, this guide will help you align your answers with practical thinking rather than theoretical explanations.
In today’s data engineering interviews, especially for tools like Spark and Databricks, interviewers are no longer interested in textbook definitions.
What they really want to assess is:
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How you apply concepts in real projects
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How you handle performance and failures
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How well you understand business impact
1. Introduction & Project Context
Interviewers usually start by understanding you and your project.
Common Questions:
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Explain about yourself along with your project architecture.
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What were your roles and responsibilities?
What they actually want:
They want you to explain:
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The business problem
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The end-to-end data flow
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Your individual contribution
Tip: Always explain your project using an architecture mindset — source → ingestion → processing → storage → consumption.
2. Databricks & Spark – Practical Understanding
Instead of asking “What is Databricks?”, interviewers may ask:
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Why did you choose Databricks for your project?
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What problem did it solve compared to traditional Spark?
They expect answers around:
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Scalability
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Performance
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Collaborative notebooks
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Integration with cloud storage
Similarly, for PySpark:
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Have you worked on PySpark?
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Which components did you actively use?
Focus on real usage:
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DataFrames & Spark SQL
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UDFs / Window functions
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Optimizations you applied
3. Spark Architecture from a Real-Time Perspective
Rather than definitions, questions are framed like:
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Explain Spark architecture.
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How does understanding Spark internals help during failures?
Interviewers want to know if you understand:
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Driver vs Executors
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Jobs, stages, and tasks
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How data shuffling impacts performance
Tip: Connect architecture knowledge to debugging slow or failed jobs.
4. Performance Challenges & Optimization
This is one of the most important sections in interviews.
Frequently asked questions:
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What is data skewness?
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Have you faced it in your project?
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How did you resolve it?
They also ask:
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Partition vs Bucketing — where did you use them?
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What does optimization mean in your project?
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Explain Z-ordering.
Interviewers expect problem-solving stories, such as:
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Identifying skew using Spark UI
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Repartitioning or salting keys
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Using Z-ordering for query performance
5. Storage Formats & Data Handling
Questions often include:
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Have you worked with Parquet?
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Why did you choose it?
Expected answers include:
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Columnar storage benefits
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Reduced I/O
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Better compression and query speed
They may also ask:
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What types of data did you handle?
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Structured vs semi-structured data
Always link storage decisions to performance and cost efficiency.
6. Memory Management & Cluster Strategy
Interviewers love asking:
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Difference between Cache and Persist
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When did you use Persist?
They want to see if you understand:
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Memory vs disk usage
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Reusability of DataFrames
Cluster-related questions:
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Types of Databricks clusters
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Why job scheduling isn’t allowed on interactive clusters?
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When interactive clusters are useful?
Tip: Explain clusters based on development vs production usage.
7. Data Architecture & Design Patterns
A very common question:
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Explain Medallion Architecture implemented in your project.
Interviewers expect:
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Bronze → Silver → Gold explanation
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Data quality checks
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Incremental processing
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Reprocessing strategy
This shows your data design maturity.
8. Monitoring, Debugging & Cost Awareness
Real-world projects don’t run perfectly.
Typical questions:
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How do you monitor CPU and memory usage?
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Which tools did you use?
Expected answers include:
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Databricks UI
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Spark UI
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Job metrics and logs
They may also ask about web grid or Spark UI usage for debugging.
9. Upskilling: AI, GenAI & Future Readiness
Modern interviews also focus on future readiness:
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How are you upskilling yourself in AI, GenAI, or LLMs?
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Are you ready to work on AI-based projects?
They’re not testing expertise — they’re testing mindset and willingness to learn.
10. Business Impact & Behavioral Questions
Some of the most critical questions:
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What happens if your data pipeline stops?
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What is the business impact?
Interviewers want to see:
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Ownership
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Priority handling
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Communication skills
Other common questions:
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Why do you want to join this company?
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Are you open to relocation?
Final Thoughts:
Spark & Databricks interviews are no longer about:
- Definitions
- Syntax memorization
They are about:
- Real-time scenarios
- Performance tuning
- Architecture decisions
- Business impact