Question 1

What SQL topics does Databricks focus on?

Accepted Answer

Databricks SQL interviews emphasize data engineering thinking: partition strategies, ETL pipeline patterns, slowly-changing dimensions (SCD Type 1/2), incremental processing, and query performance tuning. Expect questions that blend SQL with scale concerns — how will this query behave on 1TB?

Question 2

Do Databricks interviews use Spark SQL or standard SQL?

Accepted Answer

Both. Standard ANSI SQL covers most of what's tested, but knowing Spark SQL specifics pays off — MERGE INTO for upserts, Delta Lake time travel, window spec optimization, and broadcast hints. You don't need to write PySpark, just understand how SQL maps to distributed execution.

Question 3

How technical are Databricks interviews?

Accepted Answer

For data engineering roles: very technical. Expect questions about query plans, partition pruning, shuffle behavior, and join strategies (broadcast vs sort-merge). For solutions engineering or analyst roles: less about internals, more about translating business needs into correct, scalable SQL.

Question 4

What should I know about Delta Lake for Databricks interviews?

Accepted Answer

Know the basics: ACID guarantees, MERGE INTO for upserts, time travel (AS OF syntax), schema enforcement, and OPTIMIZE/VACUUM operations. You don't need deep internals, but being able to reach for MERGE when appropriate vs INSERT+UPDATE is expected.

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