Topic – Crack Databricks and Spark Interview Questions
This e-book is the only resource that you need to master Spark and Delta Lake. Trust the resource and give it a strong try to cover everything end to end
Topics Covered
|
Part |
Topic |
Approx. Items |
|
1 |
Spark internals: RDDs, DataFrames, Datasets, DAG, stages, tasks |
90 |
|
2 |
Spark performance: shuffles, partitioning, skew, AQE, broadcast joins, caching |
90 |
|
3 |
Spark Structured Streaming: watermarks, state stores, exactly-once semantics |
60 |
|
4 |
Delta Lake: ACID, time travel, MERGE, Z-ORDER, OPTIMIZE, VACUUM, CDC |
70 |
|
5 |
Databricks platform: clusters, Unity Catalog, Workflows, DLT, Photon, Auto Loader |
80 |
|
6 |
Architecture and design: medallion layers, lakehouse patterns, cost tuning |
50 |
|
7 |
Scenario and debugging: OOM errors, small-file problems, slow jobs |
60 |
|
8 |
MCQs with explanations |
100 |
Question Types
- Conceptual: “Why does Spark use lazy evaluation?”
- MCQ: four options with explained answers
- Scenario: “A join stage runs 6 hours with one task taking 90% of the time. Diagnose it.”
- Design: “Build a CDC pipeline into a Delta table with late-arriving data.”
- Trade-off: Photon versus standard runtime, or Delta versus Parquet
Target Designations
- Data Engineer (2–5 years)
- Senior and Staff Data Engineer
- Analytics Engineer with Spark exposure
- Big Data Architect
- Lakehouse Solutions Architect
Target Companies
Product companies and GCCs with active Databricks footprints, such as Databricks itself, Microsoft, Amazon, Uber, Netflix, Walmart Global Tech, and large consultancies and GCCs in India. Check this list against current hiring before publishing.
CTC Targets (India, approximate, per year)
|
Level |
Experience |
Range |
|
Data Engineer |
2–5 yrs |
₹12–25 LPA |
|
Senior Data Engineer |
5–8 yrs |
₹25–45 LPA |
|
Staff Data Engineer |
8+ yrs |
₹45–80+ LPA |
