Azure Databricks interview questions usually cover six areas: workspace and clusters, Spark and PySpark, Delta Lake, ingestion with Auto Loader and pipelines, performance tuning, and Unity Catalog security. Freshers are tested on concepts and PySpark basics; experienced candidates get optimisation and scenario-based design questions.
📑 On this page · 10 sections
What interview panels ask about Azure Databricks
Azure Databricks is where most Azure data engineering interviews get technical. Azure Data Factory moves and orchestrates data, but the heavy transformation work — joins over billions of rows, incremental merges, data quality checks — usually runs on Spark in Databricks. That is why panels spend a large part of the technical round here.
Expect three layers of questions. First, concepts: what a cluster is, how Spark executes a job, what Delta Lake adds to Parquet. Second, hands-on PySpark: write a transformation, fix a join, handle nulls and duplicates. Third, design and tuning: why a job is slow, how to load incrementally, how to secure data with Unity Catalog.
Read the questions in order if you are a fresher. If you already work with Databricks, skim the basics and spend your time on the performance and scenario sections. For the wider interview — SQL, ADF, Synapse and Fabric — see our Azure Data Engineer interview questions guide. If you want to learn this hands-on, join our Azure Data Engineer Training in Hyderabad. Official references: Azure Databricks documentation on Microsoft Learn and the Delta Lake documentation.
🧱 Six sections, Q1 to Q36, each with an answer, a real-time example and an interview tip. Practise Azure Databricks interview questions with live labs.
Book a free demoAzure Databricks basics and clusters
These questions check that you understand the platform before you write any code.
Q1. What is Azure Databricks?
🌱 Fresher · In simple words: Databricks is a ready-made Spark platform on Azure where you clean and transform big data using notebooks.
Answer. Azure Databricks is a managed Apache Spark–based data and analytics platform offered as a first-party Azure service. It provides collaborative notebooks, managed clusters, jobs, Delta Lake storage and Unity Catalog governance, and integrates with ADLS Gen2, Azure Data Factory, Microsoft Entra ID and Key Vault.
Real-time example. A retail team lands raw sales files in ADLS Gen2, then uses Databricks notebooks to clean, join and aggregate them into Delta tables that Power BI reads.
Interview tip. Say “managed Spark plus Delta Lake plus governance” — that one line shows you know it is more than hosted Spark.
Q2. What is the difference between the control plane and the compute plane?
🚀 Experienced · In simple words: The control plane is the Databricks “brain” (UI, notebooks, jobs). The compute plane is the “muscle” where your data is actually processed.
Answer. The control plane is managed by Databricks and holds the web application, notebooks, job scheduler and cluster manager. The compute plane is where clusters actually run and process data — in classic compute it runs in your Azure subscription, and for serverless compute it runs in Databricks-managed infrastructure.
Real-time example. A bank deploys the workspace with VNet injection so that classic cluster VMs run inside its own virtual network and reach storage over private endpoints.
Interview tip. Mention that data is processed in the compute plane; panels use this to check your security awareness.
Q3. All-purpose cluster vs job cluster — when do you use each?
🌱 Fresher · In simple words: Use an all-purpose cluster while you develop and test. Use a job cluster to run scheduled production jobs at lower cost.
Answer. An all-purpose cluster is created manually and shared for interactive development in notebooks. A job cluster is created when a scheduled job starts and terminated when it finishes, so it is cheaper and isolated for production workloads.
Real-time example. Developers explore data on a small all-purpose cluster with auto-termination, while the nightly pipeline runs on a job cluster defined in the job configuration.
Interview tip. Always add: “production jobs should run on job clusters or serverless jobs, not shared all-purpose clusters.” See also types of clusters in Databricks.
Q4. What are the driver and worker nodes?
🌱 Fresher · In simple words: The driver is the manager that plans the work. The workers are the team that does the work in parallel.
Answer. The driver runs your main program, builds the execution plan and schedules tasks. Worker nodes run executors that process partitions of the data in parallel and return results to the driver.
Real-time example. Calling collect() on a 50 GB DataFrame pulls all rows to the driver and crashes it with an out-of-memory error — the classic fresher mistake.
Interview tip. Link the answer to a failure you know: driver OOM from collect() or large broadcast variables.
Q5. What is autoscaling and auto-termination?
🌱 Fresher · In simple words: Autoscaling adds or removes machines as the load changes. Auto-termination switches the cluster off when nobody is using it, to save money.
Answer. Autoscaling lets a cluster add or remove workers between a minimum and maximum based on load. Auto-termination shuts a cluster down after a set period of inactivity so you stop paying for idle compute.
Real-time example. An all-purpose cluster set to 2–8 workers and 30 minutes of auto-termination keeps development costs predictable.
Interview tip. Cost control is a favourite follow-up; mention cluster policies too.
Q6. What is the Databricks Runtime and Photon?
🚀 Experienced · In simple words: The runtime is the software package installed on a cluster. Photon is a faster engine that makes many queries run quicker.
Answer. The Databricks Runtime is the set of components installed on a cluster — Apache Spark plus Delta Lake, libraries and optimisations — released in versioned and LTS editions. Photon is a vectorised query engine written in C++ that speeds up SQL and DataFrame workloads on supported operations.
Real-time example. A team pins its production jobs to an LTS runtime so that library behaviour does not change unexpectedly.
Interview tip. Say why LTS matters for production stability.
Spark and PySpark interview questions
Most coding rounds happen here. Practise writing these answers in a notebook, not only reading them.
Q7. What are transformations and actions? What is lazy evaluation?
🌱 Fresher · In simple words: Transformations are the plan; actions are the “go” button. Spark does nothing until you press “go”.
Answer. Transformations such as select, filter and join define a new DataFrame but do not run immediately. Actions such as count, show, collect and write trigger execution. Spark builds a logical plan from all transformations and optimises it only when an action is called — that is lazy evaluation.
Real-time example. Chaining ten filters and selects costs nothing until the final write; Spark then pushes filters down and reads only the needed columns.
Interview tip. Mention the Catalyst optimiser (Spark’s built-in query planner) to show you know why laziness is useful.
Q8. Narrow vs wide transformations?
🌱 Fresher · In simple words: Narrow means each machine works only on its own data. Wide means data has to move between machines, which is slower.
Answer. Narrow transformations (filter, select, withColumn) work within a partition with no data movement. Wide transformations (groupBy, join, distinct, orderBy) need a shuffle, which moves data across the network between stages.
Real-time example. A groupBy on customer_id creates a new stage boundary in the Spark UI because rows must be shuffled by key.
Interview tip. Then say: “shuffles are the main cost I try to reduce.”
Q9. repartition() vs coalesce()?
🌱 Fresher · In simple words: repartition() reshuffles data into new, even parts. coalesce() just merges existing parts into fewer parts, which is cheaper.
Answer. repartition() can increase or decrease partitions and performs a full shuffle to distribute data evenly. coalesce() only reduces partitions by merging existing ones without a full shuffle, so it is cheaper but can leave partitions uneven.
Real-time example. Before writing a small daily output, coalesce(4) avoids creating hundreds of tiny files.
Interview tip. Mention the small-files problem — it shows real project experience.
Q10. cache() vs persist()?
🌱 Fresher · In simple words: Both keep a DataFrame ready in memory so Spark does not recompute it. persist() also lets you choose where it is stored.
Answer. Both keep a DataFrame for reuse across multiple actions. cache() uses the default storage level, while persist() lets you choose the storage level, such as memory only or memory and disk. Use them only when a DataFrame is reused, and unpersist when done.
Real-time example. A cleaned customer DataFrame used by five downstream aggregations is cached once instead of being recomputed five times.
Interview tip. Say when NOT to cache — single-use DataFrames waste memory.
Q11. How do you remove duplicates in PySpark?
🌱 Fresher · In simple words: Keep only one row per business key — with dropDuplicates(), or by ranking the rows and keeping the latest one.
Answer. Use dropDuplicates() on the business key columns, or use a window function with row_number() partitioned by the key and ordered by a timestamp, then keep row 1 to retain the latest record.
Real-time example. Order events arriving twice from a source are de-duplicated by order_id, keeping the record with the latest updated_at.
Interview tip. Write the window-function version; it shows you can control which duplicate survives.
Q12. How do you handle schema evolution when reading new files?
🚀 Experienced · In simple words: When the source adds or changes columns, decide how your table accepts the change, instead of letting it break or change silently.
Answer. Define an explicit schema for production reads instead of relying on inference. For Delta writes, enable schema evolution deliberately with the mergeSchema option or automatic schema evolution for MERGE, and capture unexpected columns with Auto Loader rescued data.
Real-time example. A source adds a discount column; mergeSchema adds it to the Delta table while old rows show null.
Interview tip. Mention that silent schema drift is a data-quality risk, so evolution should be controlled.
Q13. What is a UDF and why should you avoid Python UDFs where possible?
🚀 Experienced · In simple words: A UDF is your own custom function. Python UDFs are slow, so use Spark’s built-in functions first.
Answer. A user-defined function applies custom logic to columns. Python UDFs move data between the JVM and Python row by row, which is slow and blocks Catalyst optimisations. Prefer built-in functions, then pandas (vectorised) UDFs if custom logic is unavoidable.
Real-time example. Replacing a Python UDF that parsed dates with to_date() cut a job’s runtime significantly.
Interview tip. The expected answer order is: built-in function, then pandas UDF, then plain Python UDF as the last resort.
Delta Lake interview questions
Delta Lake questions appear in almost every Databricks interview because Delta is the default table format.
Q14. What is Delta Lake and what does it add over Parquet?
🌱 Fresher · In simple words: Delta Lake = Parquet files + a log of every change. That log makes your data lake safe and reliable, like a database.
Answer. Delta Lake is an open table format that stores data as Parquet files plus a transaction log (_delta_log). The log adds ACID transactions, schema enforcement, time travel, MERGE/UPDATE/DELETE support and scalable metadata handling.
Real-time example. Two jobs writing to the same table no longer corrupt it, because each commit is recorded atomically in the transaction log.
Interview tip. Name the transaction log — that is the core of the answer.
Q15. What is time travel?
🌱 Fresher · In simple words: Time travel lets you see your table exactly as it was at an older version or time.
Answer. Time travel lets you query an earlier version of a Delta table using VERSION AS OF or TIMESTAMP AS OF, as long as the old data files have not been removed by VACUUM.
Real-time example. After a bad load, a team compares today’s table with VERSION AS OF the previous day and restores it with RESTORE TABLE.
Interview tip. Connect time travel to VACUUM retention to show you know the limit.
Q16. What do OPTIMIZE, Z-ORDER and VACUUM do?
🚀 Experienced · In simple words: OPTIMIZE joins many small files into bigger ones, Z-ORDER arranges data for faster filters, and VACUUM deletes old files that are no longer used.
Answer. OPTIMIZE compacts small files into larger ones. Z-ORDER co-locates related values in the same files so queries that filter on those columns skip more data. VACUUM deletes data files no longer referenced by the table and older than the retention threshold, which defaults to 7 days.
Real-time example. A streaming table that writes many small files is optimised nightly, and VACUUM runs weekly to control storage cost.
Interview tip. Mention liquid clustering as the newer alternative to partitioning plus Z-ORDER for new tables.
Q17. How do you implement SCD Type 2 in Databricks?
🚀 Experienced · In simple words: SCD Type 2 keeps history: when a value changes, close the old row and add a new row.
Answer. Use MERGE INTO on the Delta dimension table: when a matching current row has changed attributes, expire it by setting end_date and is_current = false, and insert the new version as the current row. New keys are inserted directly.
Real-time example. When a customer changes city, the old row is closed with an end date and a new row with the new city becomes current, so historic sales still report the old city.
Interview tip. Be ready to write the MERGE. Panels often ask for it on a whiteboard.
Q18. What is the medallion architecture?
🌱 Fresher · In simple words: Bronze = raw data, Silver = cleaned data, Gold = business-ready data for reports.
Answer. It organises data in layers: Bronze holds raw ingested data, Silver holds cleaned and conformed data, and Gold holds business-level aggregates and models for reporting. Each layer is usually a set of Delta tables.
Real-time example. Raw JSON clickstream lands in Bronze, is parsed and de-duplicated in Silver, and becomes daily session metrics in Gold.
Interview tip. Explain what quality checks happen between layers. Read more in our medallion architecture guide.
Q19. Managed vs external tables?
🌱 Fresher · In simple words: Drop a managed table and the data is deleted too. Drop an external table and only the table definition is removed.
Answer. For a managed table, Unity Catalog or the metastore manages both the metadata and the data location, and dropping the table removes the data. For an external table, the data lives in a path you control, and dropping the table removes only the metadata.
Real-time example. Shared landing data that other tools also read is registered as an external table, while curated Gold tables are managed.
Interview tip. Always state what DROP TABLE does in each case.
Ingestion, pipelines and orchestration
These questions test how you build reliable, incremental pipelines rather than one-off notebooks.
Q20. What is Auto Loader?
🌱 Fresher · In simple words: Auto Loader automatically picks up only the new files that land in storage.
Answer. Auto Loader (the cloudFiles source) incrementally and efficiently processes new files as they arrive in cloud storage. It tracks which files have been processed, supports schema inference and evolution, and can run continuously or as a scheduled batch with trigger availableNow.
Real-time example. Thousands of daily CSV files landing in ADLS Gen2 are picked up by Auto Loader and appended to a Bronze Delta table without reprocessing old files.
Interview tip. Contrast it with listing the folder yourself — Auto Loader handles state and scale for you.
Q21. What are Lakeflow Declarative Pipelines (formerly Delta Live Tables)?
🚀 Experienced · In simple words: You describe the tables you want and the quality rules; Databricks takes care of running them in the right order, with retries.
Answer. It is a declarative framework where you define tables and the transformations between them, and Databricks manages dependencies, orchestration, retries and data-quality expectations. Expectations can warn, drop invalid rows or fail the update.
Real-time example. A Silver table declares an expectation that order_amount is not negative and drops rows that fail it, while the pipeline reports how many were dropped.
Interview tip. Mention both names — many job descriptions still say DLT.
Q22. How do you schedule and orchestrate Databricks work?
🌱 Fresher · In simple words: Use Databricks Jobs to run notebooks on a schedule, or call them from Azure Data Factory.
Answer. Use Databricks Jobs (Lakeflow Jobs) to run notebooks, scripts or pipelines as tasks with dependencies, retries, parameters and schedules. Many Azure teams also call Databricks from Azure Data Factory, which orchestrates wider pipelines across services.
Real-time example. ADF copies data from an on-premises SQL Server, then triggers a Databricks job that transforms it into Delta tables.
Interview tip. Explain when you would choose ADF vs Databricks Jobs. See ADF vs Databricks.
Q23. How do you implement incremental loading?
🌱 Fresher · In simple words: Load only the new or changed data each time, instead of reloading everything.
Answer. Track a watermark (such as last_modified or an increasing ID), read only rows newer than the stored watermark, MERGE them into the target and update the watermark after a successful run. For files, use Auto Loader; for Delta sources, Change Data Feed can provide row-level changes.
Real-time example. A nightly job reads only orders updated since the previous run’s max timestamp and merges them into Silver.
Interview tip. Mention idempotency (safe to rerun) — rerunning the same load must not create duplicates.
Q24. How do you pass parameters to a notebook?
🌱 Fresher · In simple words: Widgets let one notebook accept inputs such as a date, so the same notebook can run for any day.
Answer. Use widgets (dbutils.widgets) in the notebook and supply values from a job task or from the ADF Databricks Notebook activity. In jobs, task parameters and task values can pass results between tasks.
Real-time example. ADF passes the load_date parameter so the same notebook processes any date during a backfill.
Interview tip. Parameterised notebooks are a sign of production thinking — say so.
Performance tuning and optimisation
Experienced candidates are judged mostly on this section. Answer with symptoms, diagnosis and fix.
Q25. A Databricks job suddenly runs much slower. How do you investigate?
🚀 Experienced · In simple words: First find which step became slow in the Spark UI, then fix that step.
Answer. Open the Spark UI and compare the slow run with a normal run: look for the stage with the longest duration, task-time skew, spill to disk, shuffle size and the number of input files. Then check whether data volume, cluster size or the query plan changed.
Real-time example. One stage had a single task running for 40 minutes while others finished in seconds — a skewed join key.
Interview tip. Structure: measure first, then fix. Panels dislike answers that jump straight to “add more nodes”.
Q26. What is data skew and how do you fix it?
🚀 Experienced · In simple words: Skew means a few keys have far too much data, so a few machines end up doing almost all the work.
Answer. Skew is when a few keys hold far more rows than others, so a few tasks do most of the work. Fixes include adaptive query execution skew-join handling, broadcasting the smaller table, salting the hot key, or filtering and processing the hot key separately.
Real-time example. Null customer IDs from guest checkouts formed one huge partition; filtering them out before the join fixed the runtime.
Interview tip. Always check for null or default keys first — it is the most common real cause.
Q27. What is a broadcast join?
🌱 Fresher · In simple words: Send the small table to every machine, so the big table does not need to move.
Answer. Spark sends a full copy of the small table to every executor so the large table can be joined without shuffling it. Spark broadcasts automatically below spark.sql.autoBroadcastJoinThreshold (10 MB by default) and you can force it with the broadcast() hint.
Real-time example. Joining a 2 billion-row fact table to a 5,000-row country table with broadcast() removed a large shuffle.
Interview tip. Warn that broadcasting a large table can crash the driver or executors.
Q28. What is Adaptive Query Execution (AQE)?
🚀 Experienced · In simple words: AQE lets Spark change its plan while the query is running, based on the real size of the data.
Answer. AQE re-optimises the query plan at runtime using actual statistics: it coalesces small shuffle partitions, switches join strategies, and splits skewed partitions. It is enabled by default in modern Spark and Databricks runtimes.
Real-time example. After a filter removes most rows, AQE switches a sort-merge join to a broadcast join automatically.
Interview tip. Say AQE helps, but it does not replace good data layout and filtering.
Q29. Should you partition every Delta table?
🚀 Experienced · In simple words: Partition only big tables, and only on a column such as date that queries usually filter on.
Answer. No. Partition only very large tables on a low-cardinality column that queries commonly filter on, such as date. Over-partitioning creates many small files and slows queries. For many new tables, liquid clustering is a more flexible option.
Real-time example. Partitioning by customer_id created millions of tiny folders; switching to date partitioning and clustering on customer_id fixed it.
Interview tip. Mention the small-files problem again — it is the core trade-off.
Q30. How do you control Databricks cost?
🌱 Fresher · In simple words: Switch off what you do not use, pick the right cluster size, and run production on job clusters.
Answer. Use job clusters or serverless for production, auto-termination and autoscaling for interactive clusters, cluster policies to restrict sizes, right-sized instance types, spot instances where interruptions are acceptable, and efficient code that reads less data.
Real-time example. Moving nightly notebooks from an always-on all-purpose cluster to job clusters reduced idle compute hours.
Interview tip. Combine technical and governance answers — policies plus code efficiency.
Unity Catalog, security and governance
Governance questions are now standard, because Unity Catalog is the default for new workspaces.
Q31. What is Unity Catalog?
🌱 Fresher · In simple words: Unity Catalog is one place to control who can see which data and to track where data came from.
Answer. Unity Catalog is the centralised governance layer for data and AI assets in Databricks. It provides a three-level namespace (catalog.schema.table), fine-grained access control, data lineage, auditing, and governed access to storage through storage credentials and external locations.
Real-time example. One metastore serves dev, test and prod workspaces, with separate catalogs for each environment.
Interview tip. Say the three-level namespace out loud — it is the quickest proof you have used it.
Q32. How do you store and use secrets?
🌱 Fresher · In simple words: Keep passwords in Azure Key Vault and read them in code with dbutils.secrets — never type them into a notebook.
Answer. Store secrets in a secret scope — commonly backed by Azure Key Vault — and read them with dbutils.secrets.get(). Secret values are redacted in notebook output and should never be hard-coded.
Real-time example. A JDBC password for a source database is kept in Key Vault and read at runtime by the notebook.
Interview tip. Hard-coded credentials in notebooks are a red flag; say how you would avoid them.
Q33. How do you implement row-level and column-level security?
🚀 Experienced · In simple words: Show each user only the rows and columns they are allowed to see.
Answer. In Unity Catalog, use row filters and column masks defined as SQL functions, or create dynamic views that check group membership with functions such as is_account_group_member().
Real-time example. Regional managers see only their region’s sales rows, and the email column is masked for analysts.
Interview tip. Give one example of each — row filter and column mask.
Practise with a trainer Mock interviews, hands-on Databricks labs and feedback in our Azure Data Engineer Training in Hyderabad.
Microsoft Fabric interview questionsScenario-based Azure Databricks interview questions
There is rarely one right answer here. Panels judge how you reason, what you ask, and which trade-offs you name.
Q34. Design a daily pipeline that loads files from ADLS Gen2 into reporting tables.
🚀 Experienced · In simple words: Pick up new files, clean them, build report tables, check quality, schedule the job and secure the data.
Answer. Ingest new files with Auto Loader into Bronze Delta tables, clean, de-duplicate and conform them into Silver with MERGE, build Gold aggregates for reporting, add data-quality checks between layers, schedule it as a job with retries and alerts, and govern access with Unity Catalog.
Real-time example. This is the same Bronze-Silver-Gold design most Azure data teams use for sales, inventory and finance data.
Interview tip. Draw the layers and name one quality check per layer.
Q35. A MERGE into a large Delta table is slow. What do you do?
🚀 Experienced · In simple words: Make MERGE read less data: filter the source and add a date or key range to the match condition.
Answer. Reduce the data the MERGE must scan: add a partition or clustering column to the join condition, filter the source to changed rows only, compact small files, and make sure the source has no duplicate keys, which also causes MERGE errors.
Real-time example. Adding the order_date range to the MERGE condition let Delta skip most files.
Interview tip. Mention file pruning (Delta skips files that cannot match) — it is the key idea.
Q36. Duplicate rows appear in Silver after a job was rerun. How do you fix and prevent it?
🚀 Experienced · In simple words: Clean the duplicates now, and change the load to MERGE so that a rerun cannot create them again.
Answer. Fix: de-duplicate the affected data by business key using a window function and rewrite or MERGE the corrected rows. Prevent: make the load idempotent (a rerun gives the same result, with no duplicates) using MERGE instead of append, track processed files or watermarks, and add a uniqueness check before publishing.
Real-time example. Replacing append writes with MERGE on order_id meant reruns updated rows instead of duplicating them.
Interview tip. Use the word idempotent — panels listen for it.
Practise these scenarios on a real workspace. If you prefer guided practice with real projects, see our Azure Data Engineer Training in Hyderabad.
Key takeaways: Azure Databricks Interview Questions
• Azure Databricks interview questions focus on clusters, PySpark, Delta Lake, ingestion, performance and Unity Catalog.
• Write PySpark code while you prepare — coding rounds expect working transformations, not definitions.
• Know MERGE, time travel, OPTIMIZE and VACUUM well; Delta Lake appears in almost every round.
• For performance questions, explain how you diagnose with the Spark UI before you suggest a fix.
• Scenario answers should name layers, quality checks, idempotency and security.
Frequently asked questions
How many Azure Databricks interview questions should I prepare?
Prepare 30 to 40 core questions well, like the 36 in this guide, and practise them in a notebook. Understanding and hands-on practice matter more than memorising hundreds of answers.
Is PySpark coding asked in Azure Databricks interviews?
Yes. Most panels ask you to write or fix PySpark transformations such as joins, aggregations, window functions and de-duplication, so practise writing code without autocomplete.
Do freshers get Databricks questions?
Yes, but at a basic level: clusters, transformations vs actions, Delta Lake basics and simple PySpark. Scenario and tuning questions are mostly for experienced candidates.
Which Databricks topics matter most for experienced candidates?
Performance tuning, data skew, MERGE optimisation, incremental loading, Unity Catalog security and pipeline design are the topics experienced candidates are judged on most.
Is Databricks covered in the DP-700 exam?
DP-700 focuses on Microsoft Fabric, which uses Spark notebooks and Delta tables, so Spark and Delta skills transfer directly. Databricks itself has separate certifications from Databricks.
Based on what we teach in our batches. Last updated 31 October 2026. We review this Azure Databricks Interview Questions guide regularly. Spotted something outdated? Tell us.