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Interview/Databricks

Compute types interview questions

Interactive Databricks interview questions on Compute types. Same topic as /learn/databricks/compute. All-purpose clusters for notebooks, job clusters that terminate, SQL warehouses for BI. Mixing them is how the bill explodes.

Lesson · Simulation

beginner
Compute types

All-purpose cluster vs jobs cluster vs SQL warehouse — pick one sentence each.

Answer it out loud, then reveal. Play steps through like the simulators.

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Production scenario

Compute types

Overnight 32-worker all-purpose

Symptoms

  • No Workflows ran
  • One notebook detached
  • Bill 8×
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All questions on this page

Indexed as FAQ. Open any item if you prefer a list to Play.

beginner

All-purpose cluster vs jobs cluster vs SQL warehouse — pick one sentence each.

Compute types · tap to open the answer

Short: All-purpose: interactive notebooks, stays on. Jobs: start, run, terminate. Warehouse: BI/Photon, not notebooks.

Detailed: Wrong compute is the expensive mistake that never shows up as a Spark bug. ETL on all-purpose burns idle hours. Tableau on an all-purpose cluster fights notebooks for slots.

Common mistake: One mega cluster for explore, ETL, and BI.

Follow-up: Which product should a nightly MERGE use?

Lesson · Simulation

intermediate

Why is an all-purpose cluster a bad host for production ETL?

Compute types · tap to open the answer

Short: It stays on, keeps leftover libraries/cache, and you pay for idle.

Detailed: A forgotten notebook pins a 32-worker cluster overnight. Job clusters get a clean Spark version and die when the task ends. Cluster policies should block oversized all-purpose.

Common mistake: Scheduling Workflows on the team's shared interactive cluster 'so it's warm'.

Follow-up: What still belongs on all-purpose?

Lesson · Simulation

senior

How do you read a Databricks bill that exploded with 'no new jobs'?

Compute types · tap to open the answer

Short: Look at all-purpose uptime, DBU SKU (Photon), and warehouses left in running.

Detailed: System tables / account console: idle clusters, SQL warehouses without auto-stop, Photon on workloads that don't benefit. Spark UI will look fine.

Senior: Auto-terminate all-purpose, job clusters only for Workflows, warehouses auto-stop, Photon where the plan is native.

Common mistake: Tuning spark.sql.shuffle.partitions to cut DBUs.

Follow-up: What policy would you add first?

Lesson · Simulation