Databricks Interview Questions (2026)
Practice 45 Databricks interview questions on Lakehouse, Delta Lake, Spark, notebooks, jobs, clusters, Unity Catalog, Workflows, Auto Loader, MLflow, and governance.
45 questions with answersWhat Is Databricks?
Key Takeaways
- ✓Databricks is a data and AI platform built around lakehouse architecture, Apache Spark, Delta Lake, governance, workflows, and ML tooling.
- ✓Interviews test Spark, Delta Lake, notebooks, clusters, jobs, Workflows, Unity Catalog, Auto Loader, MLflow, and cost control.
- ✓Strong answers separate the Databricks platform from Spark itself. Spark is the engine; Databricks adds managed workspace, governance, jobs, and lakehouse tooling.
- ✓Production answers should cover data layout, access control, lineage, job monitoring, cluster sizing, and rollback.
Databricks is a managed data and AI platform for building lakehouse, data engineering, analytics, and machine learning workloads. It combines Apache Spark, Delta Lake, notebooks, jobs, clusters, Unity Catalog, Workflows, SQL, and MLflow-style model lifecycle tools. In interviews, Databricks questions test whether you can design reliable pipelines, govern data, tune jobs, and operate costs.
Watch: Using Apache Spark to analyze open data
Video: Using Apache Spark to analyze open data (Databricks, YouTube)
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All Questions on This Page
45 questions- 1. What is a Databricks lakehouse?
- 2. What is Delta Lake?
- 3. What is medallion architecture?
- 4. What is Unity Catalog?
- 5. What is a Databricks cluster?
- 6. What is a Databricks job?
- 7. What are Databricks Workflows?
- 8. What is Auto Loader?
- 9. What does OPTIMIZE do?
- 10. What does VACUUM do?
- 11. What is Delta time travel?
- 12. How does schema evolution matter?
- 13. How should notebooks be used in production?
- 14. Where does MLflow fit?
- 15. What is a cluster policy?
- 16. Design a Databricks ingestion pipeline.
- 17. How do you debug a slow Databricks job?
- 18. How do you fix small files in Delta tables?
- 19. How do you govern access?
- 20. How do you design bronze, silver, and gold layers?
- 21. How do you handle schema drift?
- 22. How do you use time travel for recovery?
- 23. How do you handle a failed Workflow?
- 24. How do you control Databricks cost?
- 25. How do you track ML work in Databricks?
- 26. When do you use MERGE?
- 27. How do you choose partitions?
- 28. How do you add data quality checks?
- 29. How does lineage help?
- 30. How do you make a notebook production-ready?
- 31. A team treats Delta like plain Parquet. What do they miss?
- 32. Queries slow down after many small writes. What helps?
- 33. A user can see raw sensitive data. What should change?
- 34. Late data changes yesterday's numbers. What do you do?
- 35. Time travel no longer works. What happened?
- 36. A tiny job runs on a huge cluster. What is wrong?
- 37. Rerunning a job duplicates rows. What is missing?
- 38. A new column breaks a downstream dashboard. What control helps?
- 39. A BI dashboard over a gold table is slow. What do you inspect?
- 40. A workflow depends on manual notebook state. What is wrong?
- 41. A table change breaks ML features. What should have helped?
- 42. A streaming job restarts from the wrong point. What do you check?
- 43. Databricks spend spikes. What do you inspect?
- 44. No one can reproduce a model. What is missing?
- 45. What do you ask before approving a Databricks design?
Databricks Fundamentals
Foundational15 questionsStart here. These are the definitions and first-principle checks that open most rounds.
Q1. What is a Databricks lakehouse?
A lakehouse combines lake storage with table management, governance, and analytics features.
It aims to support engineering, BI, and ML on shared governed data.
Lakehouse changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Watch a deeper explanation
Video: Using Apache Spark to analyze open data (Databricks, YouTube)
Q2. What is Delta Lake?
Delta Lake is an open table format that adds ACID transactions, schema handling, time travel, and metadata to data lake tables.
It is more than plain Parquet.
Delta Lake changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Lakehouse pipeline path
Layer names should map to quality, not decoration.
Q3. What is medallion architecture?
Medallion architecture organizes data into bronze, silver, and gold layers by quality and use.
Bronze is raw, silver is cleaned, and gold is business-ready.
Medallion architecture changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q4. What is Unity Catalog?
Unity Catalog is Databricks governance for catalogs, schemas, tables, permissions, lineage, and discovery.
It centralizes access control.
Unity Catalog changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
| Answer part | What to say | Evidence to mention |
|---|---|---|
| Definition | Unity Catalog in one direct sentence. | Official docs or course material |
| Use case | The work where it changes a decision. | Dataset, model, query, dashboard, or pipeline |
| Risk | What breaks when it is misunderstood. | Metric, log, test result, or review note |
Q5. What is a Databricks cluster?
A cluster is compute used to run notebooks, jobs, and Spark workloads.
Sizing affects cost and performance.
Cluster changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Watch a deeper explanation
Video: Databricks Lakehouse overview (Databricks, YouTube)
Q6. What is a Databricks job?
A job runs a notebook, Python file, SQL task, JAR, or workflow task on a schedule or trigger.
Jobs need retries, alerts, and parameters.
Job changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q7. What are Databricks Workflows?
Workflows orchestrate multi-task jobs and dependencies inside Databricks.
They can replace external orchestration for many platform-native pipelines.
Workflows changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q8. What is Auto Loader?
Auto Loader incrementally ingests new files from cloud storage into Delta tables.
It handles file discovery and schema inference or evolution patterns.
Auto Loader changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q9. What does OPTIMIZE do?
OPTIMIZE compacts small files and can improve query performance on Delta tables.
It is table maintenance, not business transformation.
OPTIMIZE changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q10. What does VACUUM do?
VACUUM removes old files no longer needed by Delta table retention rules.
Retention settings affect time travel and recovery.
VACUUM changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q11. What is Delta time travel?
Time travel lets users query older versions of a Delta table.
It helps rollback, auditing, and debugging.
Time travel changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q12. How does schema evolution matter?
Schema evolution handles changes in incoming columns or types according to configured rules.
Uncontrolled evolution can break consumers.
Schema evolution changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q13. How should notebooks be used in production?
Production notebooks should be parameterized, versioned, reviewed, scheduled, and monitored.
Ad hoc notebooks are not enough for production.
Notebook changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Watch a deeper explanation
Video: Delta Lake explained (Databricks, YouTube)
Q14. Where does MLflow fit?
MLflow tracks experiments, models, metrics, artifacts, and model lifecycle work.
Databricks integrates MLflow workflows.
MLflow changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q15. What is a cluster policy?
A cluster policy controls allowed cluster settings to manage cost, security, and standards.
Policies help teams avoid unsafe or expensive compute.
Cluster policy changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Databricks Practical Interview Questions
Intermediate15 questionsThese questions test whether you can apply the topic to real data, real code, and messy constraints.
Q16. Design a Databricks ingestion pipeline.
Use Auto Loader or batch reads, land raw data into bronze Delta, validate schema, write silver tables, and schedule with Workflows.
Add alerts for schema and freshness failures.
ingestion pipeline changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
# Interview check: split data, run baseline, inspect metric
from sklearn.metrics import accuracy_score
print(accuracy_score(y_true, y_pred))Q17. How do you debug a slow Databricks job?
Check Spark UI, stages, shuffles, skew, file sizes, partitions, cluster size, caching, and query plan.
Measure bottlenecks before scaling compute.
slow Spark job changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q18. How do you fix small files in Delta tables?
Control write partitions, compact with OPTIMIZE, and review ingestion frequency and partition design.
Small files slow reads and metadata operations.
small files changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q19. How do you govern access?
Use Unity Catalog permissions at catalog, schema, table, view, and column levels where needed.
Do not share raw sensitive tables broadly.
access control changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q20. How do you design bronze, silver, and gold layers?
Keep bronze raw, silver cleaned and validated, and gold aligned to business metrics or serving use cases.
Layer quality should be explicit.
medallion design changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q21. How do you handle schema drift?
Detect schema changes, decide allowed evolution, alert owners, and protect downstream tables with contracts.
Not every new field should flow silently.
schema drift changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q22. How do you use time travel for recovery?
Identify good table version, validate it, restore or query old version, and document downstream impact.
Retention must allow recovery.
time travel rollback changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q23. How do you handle a failed Workflow?
Check task logs, cluster logs, dependencies, parameters, retries, and data freshness, then rerun the safe task range.
Reruns need idempotent writes.
workflow failure changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q24. How do you control Databricks cost?
Use job clusters, autoscaling, cluster policies, right-sized compute, table optimization, and cost attribution tags.
Poor table layout can waste compute.
cost control changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q25. How do you track ML work in Databricks?
Log parameters, metrics, artifacts, data version, code version, and model artifact through MLflow.
Tracking supports reproducibility.
ML experiment changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Watch a deeper explanation
Video: Unity Catalog overview (Databricks, YouTube)
Q26. When do you use MERGE?
Use MERGE for upserts, CDC, deduplication, and late-arriving updates with a unique key.
Bad merge keys cause duplicates or overwrites.
Delta merge changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q27. How do you choose partitions?
Partition by common low-cardinality filters such as date, avoid high-cardinality columns, and monitor file size.
Bad partitions create small files.
partition design changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q28. How do you add data quality checks?
Check schema, nulls, accepted values, duplicates, counts, freshness, and reconciliation before publishing gold tables.
Gold tables need stronger gates.
data quality changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q29. How does lineage help?
Use Unity Catalog lineage to see upstream and downstream dependencies before changes.
Lineage supports impact analysis.
lineage changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q30. How do you make a notebook production-ready?
Parameterize it, remove manual steps, add checks, version it, schedule it, and alert on failure.
Production should not depend on clicking cells.
production notebook changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Databricks Advanced Scenarios
Advanced15 questionsAdvanced rounds test trade-offs, failure modes, and whether the decision can hold up under production pressure.
Q31. A team treats Delta like plain Parquet. What do they miss?
They may miss transactions, schema enforcement, time travel, table history, and maintenance features.
Delta adds table behavior.
plain parquet issue changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q32. Queries slow down after many small writes. What helps?
Optimize file sizes, reduce write frequency, and tune partitioning.
Small files are a common lakehouse issue.
small file slowdown changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q33. A user can see raw sensitive data. What should change?
Use Unity Catalog permissions, masking or views, and least-privilege access.
Governance is not optional.
wrong permissions changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q34. Late data changes yesterday's numbers. What do you do?
Use merge or backfill logic, validate metrics, and communicate downstream changes.
Business tables need late-data policy.
late-arriving data changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q35. Time travel no longer works. What happened?
VACUUM retention may have removed older files.
Retention settings affect recovery.
vacuum too aggressive changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q36. A tiny job runs on a huge cluster. What is wrong?
Compute is oversized for the workload.
Right-size job clusters and use policies.
cluster overkill changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q37. Rerunning a job duplicates rows. What is missing?
Writes are not idempotent or merge keys are wrong.
Reruns should be safe.
workflow rerun duplicates changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q38. A new column breaks a downstream dashboard. What control helps?
Schema contracts, controlled evolution, tests, and owner alerts.
Evolution needs governance.
schema evolution break changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q39. A BI dashboard over a gold table is slow. What do you inspect?
Check file layout, partitions, query filters, table size, materialization, and clustering options.
Gold tables should be query-friendly.
unoptimized gold table changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q40. A workflow depends on manual notebook state. What is wrong?
The job is not reproducible.
Parameters and versioned code are needed.
notebook dependency changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q41. A table change breaks ML features. What should have helped?
Unity Catalog lineage and impact review should identify downstream users.
Platform metadata prevents surprises.
missing lineage changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q42. A streaming job restarts from the wrong point. What do you check?
Check checkpoint path, query id, source offsets, and sink idempotency.
Checkpoint location is part of state.
stream checkpoint issue changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q43. Databricks spend spikes. What do you inspect?
Check jobs, clusters, warehouses, autoscaling, table scans, small files, and user notebooks.
Cost needs attribution.
cost spike changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q44. No one can reproduce a model. What is missing?
Experiment tracking with data, code, parameters, metrics, and artifacts is missing.
MLflow-style tracking solves this.
ML model tracking missing changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Q45. What do you ask before approving a Databricks design?
Ask about Delta layout, medallion layers, Unity Catalog, job orchestration, data quality, cluster policy, cost, lineage, and rollback.
These decide platform readiness.
senior review changes Databricks decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.
Databricks vs Spark vs Delta Lake
Candidates often mix these terms. Databricks is the platform, Spark is the processing engine, and Delta Lake is the table storage layer.
| Term | Role | Example | Interview trap |
|---|---|---|---|
| Databricks | Managed platform | Workspace, jobs, clusters, governance | Calling it only Spark |
| Apache Spark | Distributed compute engine | DataFrames and Spark SQL | Ignoring platform services |
| Delta Lake | Lakehouse table format | ACID tables on object storage | Treating it like plain Parquet |
| Unity Catalog | Governance layer | Permissions, lineage, catalogs | Skipping access design |
Databricks answer signals
Good answers connect Spark code with platform operations.
- Spark execution: jobs, shuffles, partitions
- Delta design: ACID, time travel, optimize
- Governance: Unity Catalog and lineage
- Operations: Workflows, clusters, cost
How to Prepare for a Databricks Interview
Prepare by tracing one data pipeline through ingestion, Delta tables, transformation, governance, workflow scheduling, and monitoring.
- Review Spark DataFrames, Spark SQL, Delta Lake, Unity Catalog, notebooks, clusters, jobs, and Workflows.
- Know Auto Loader, medallion architecture, partitioning, OPTIMIZE, VACUUM, time travel, and schema evolution.
- Practice job troubleshooting with Spark UI, cluster logs, data quality checks, and cost review.
- One story about slow jobs, small files, late data, permissions, or failed production workflows is useful.
Databricks interview prep flow
Most rounds reward clear reasoning more than memorized phrasing.
How Strong Databricks Answers Sound
A strong Databricks answer explains both the data logic and the platform control. Say how data lands, which Delta tables are created, how schema changes are handled, how access is governed, how jobs are scheduled, and how failures are monitored. production-ready answers mention medallion layers, Unity Catalog, lineage, cluster policies, small files, optimize, vacuum, time travel, job retries, and cost attribution.
Use Delta intentionally
Delta tables add transactions, schema handling, time travel, and table maintenance on top of lake storage.
Govern before sharing
Unity Catalog permissions and lineage should be planned before many teams use the data.
Treat notebooks as code
Production notebooks need parameters, reviews, jobs, tests, and ownership.
Tune data layout
Partitions, file sizes, clustering, optimize, and query filters decide cost and speed.
Test Yourself: Databricks Quiz
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Frequently Asked Questions
What do Databricks interviews usually ask?
Is Databricks the same as Spark?
What Databricks project should I discuss?
What separates experienced Databricks candidates?
Practice Databricks answers before data platform rounds
Use this Databricks bank for Spark, lakehouse, and governance answers, then practice SQL and Python tasks with Hyring's AI Coding Interviewer.
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Written by Adithyan RK