Real questions from top companies · hard
Describe the data pipeline architecture you've worked with.
Explain the trade-offs between batch and real-time data processing. Provide examples of when each is appropriate.
Have you worked on Data Warehousing projects?
Retrieve the most recent sale_timestamp for each product (Latest Transaction).
What is the difference between OLTP and OLAP?
Difference Between Internal and External Tables in BigQuery
How do you optimize a long-running SQL query?
Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.
Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.
Design an anti-skew strategy for a join on a high-cardinality key with a long-tail distribution (e.g., a few keys hold 80% of rows). Cover salting, split-skew, AQE, and cost/operational trade-offs.
Prioritize Spark optimizations by impact and effort. Discuss partitioning strategy, caching policy, join selection, shuffle reduction, and when each becomes a scalability or cost bottleneck.
Walk through the three AQE features in Spark 3.x (coalesce, join switch, skew join)—how they operate at shuffle boundaries, which configs enable them, and what happens when AQE cannot help.
Explain wide vs. narrow transformations and how they drive shuffle cost, failure domains, and pipeline design. When would you intentionally add a wide transformation, and how do you minimize its impact?
Design a Delta table layout for mixed workload: point lookups by user_id, range scans by date, and full partition scans. Compare partitioning vs. Z-ordering—when to use each, and the rewrite cost trade-off.
Architecturally, how do Job–Stage–Task boundaries in Spark's execution model impact cluster sizing, shuffle cost, and when would you deliberately collapse or split stages?
Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.
Explain the Medallion Architecture (Bronze, Silver, Gold layers).
Explain the benefits of using DataFrames over RDDs.
Explain the concept of checkpointing in Spark and why it is important.
Explain the difference between batch and streaming data processing in Data Fusion.
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.