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Databricks MCQs (Multiple-Choice Questions)

Practice Databricks MCQs covering Lakehouse architecture, Delta Lake, Unity Catalog, Databricks SQL, Apache Spark, notebooks, compute, Auto Loader, Structured Streaming, Lakeflow, Jobs, and data governance.

Databricks MCQs

These Databricks multiple-choice questions are designed to test your understanding of the platform, data engineering workflows, analytics, storage, processing, governance, and modern lakehouse architecture.

List of Databricks MCQs

The following questions cover fundamental as well as advanced Databricks concepts and features.

1. What is Databricks primarily designed for?

  1. Data and AI workloads on a lakehouse platform
  2. Operating system development
  3. Web browser development
  4. Domain name registration

Answer: A) Data and AI workloads on a lakehouse platform

Explanation:

Databricks provides an integrated platform for data engineering, analytics, machine learning, AI, and related workloads using a lakehouse architecture.

2. What is a data lakehouse?

  1. A system combining characteristics of data lakes and data warehouses
  2. A relational database used only for transactions
  3. A network storage protocol
  4. A programming language

Answer: A) A system combining characteristics of data lakes and data warehouses

Explanation:

A lakehouse combines scalable data-lake storage with capabilities traditionally associated with data warehouses and analytical systems.

3. Which technology provides the transactional storage layer for Databricks lakehouse tables?

  1. Delta Lake
  2. Apache HTTP Server
  3. Redis
  4. DNS

Answer: A) Delta Lake

Explanation:

Delta Lake is the optimized storage layer that provides the foundation for tables in the Databricks lakehouse.

4. What does Delta Lake add to Parquet files?

  1. A transaction log and support for ACID transactions
  2. A web server
  3. A DNS resolver
  4. A Python interpreter

Answer: A) A transaction log and support for ACID transactions

Explanation:

Delta Lake extends Parquet data files with a file-based transaction log that enables transactional guarantees and scalable metadata handling.

5. What does ACID provide for Delta Lake transactions?

  1. Transactional consistency and reliability
  2. Network routing
  3. HTML rendering
  4. Operating system virtualization

Answer: A) Transactional consistency and reliability

Explanation:

ACID transaction support helps ensure that changes to Delta tables are applied consistently and reliably, including when multiple operations interact with the same data.

6. What is the Delta Lake transaction log commonly used for?

  1. Tracking table changes and versions
  2. Managing DNS records
  3. Compiling Python code
  4. Rendering dashboards

Answer: A) Tracking table changes and versions

Explanation:

The Delta transaction log records changes to a table and enables operations such as table history and version-based querying.

7. What is Delta Lake time travel?

  1. The ability to query previous versions of a Delta table
  2. A method for scheduling jobs
  3. A streaming ingestion protocol
  4. A compute autoscaling feature

Answer: A) The ability to query previous versions of a Delta table

Explanation:

Each write creates a new table version, allowing previous versions to be inspected or queried when the required data and transaction-log retention are available.

8. Which command can be used to inspect the history of a Delta table?

  1. DESCRIBE HISTORY
  2. SHOW DNS
  3. LIST NETWORK
  4. PRINT HISTORY

Answer: A) DESCRIBE HISTORY

Explanation:

DESCRIBE HISTORY provides information about operations and versions recorded for a Delta table.

9. What is Unity Catalog?

  1. A unified governance layer for data and AI assets
  2. A Spark execution engine
  3. A file compression format
  4. A Python package manager

Answer: A) A unified governance layer for data and AI assets

Explanation:

Unity Catalog provides centralized governance capabilities including access control, lineage, auditing, and data discovery across Databricks workspaces.

10. Which hierarchy is commonly used for objects in Unity Catalog?

  1. Catalog, schema, object
  2. Server, CPU, process
  3. Region, router, packet
  4. Project, compiler, binary

Answer: A) Catalog, schema, object

Explanation:

Unity Catalog organizes data assets using a hierarchical namespace, with catalogs containing schemas and schemas containing objects such as tables and views.

11. What is one major purpose of Unity Catalog access control?

  1. Controlling who can access governed data and objects
  2. Increasing CPU clock speed
  3. Compressing Parquet files
  4. Scheduling Spark executors

Answer: A) Controlling who can access governed data and objects

Explanation:

Unity Catalog provides centralized permissions and access control for governed data and AI assets.

12. Which capability helps users understand where data originated and how it was transformed?

  1. Data lineage
  2. Autoscaling
  3. Partition pruning
  4. Cluster pooling

Answer: A) Data lineage

Explanation:

Data lineage provides information about relationships and movement between data assets and helps users understand data dependencies.

13. What is a Databricks notebook?

  1. An interactive environment for writing and executing code
  2. A physical storage device
  3. A database backup format
  4. A network router

Answer: A) An interactive environment for writing and executing code

Explanation:

Databricks notebooks provide an interactive environment for developing and running data, analytics, and AI workloads.

14. Which languages can commonly be used in Databricks notebooks?

  1. Python, SQL, Scala, and R
  2. Only HTML
  3. Only JavaScript
  4. Only CSS

Answer: A) Python, SQL, Scala, and R

Explanation:

Databricks notebooks support multiple languages, including Python, SQL, Scala, and R, depending on the workload and environment.

15. What provides the compute resources required to execute Databricks workloads?

  1. Compute resources
  2. Unity Catalog only
  3. Delta transaction logs
  4. Workspace folders only

Answer: A) Compute resources

Explanation:

Databricks workloads execute using compute resources such as serverless compute, all-purpose compute, or SQL warehouses depending on the workload.

16. What is serverless compute in Databricks?

  1. Compute infrastructure managed by Databricks for supported workloads
  2. A local computer with no network connection
  3. A storage-only service
  4. A database schema

Answer: A) Compute infrastructure managed by Databricks for supported workloads

Explanation:

Serverless compute allows supported workloads to run on Databricks-managed infrastructure without users having to manage the underlying compute resources.

17. Which compute type is optimized for SQL analytics in Databricks?

  1. SQL warehouse
  2. DataNode
  3. Notebook folder
  4. Delta log

Answer: A) SQL warehouse

Explanation:

SQL warehouses provide compute resources optimized for Databricks SQL workloads and analytical queries.

18. What is Databricks SQL primarily used for?

  1. SQL analytics and data warehousing
  2. Operating system installation
  3. Network packet routing
  4. Mobile application compilation

Answer: A) SQL analytics and data warehousing

Explanation:

Databricks SQL provides a cloud data warehouse experience built on lakehouse architecture and supports SQL analytics directly on lake data.

19. Which SQL engine capability allows Databricks SQL to query data without first moving it into a traditional separate warehouse?

  1. Querying directly on the lakehouse data
  2. Browser caching
  3. DNS forwarding
  4. Client-side rendering

Answer: A) Querying directly on the lakehouse data

Explanation:

Databricks SQL is designed to run queries directly on data in the lakehouse rather than requiring a separate copy solely for warehouse querying.

20. What is Apache Spark's role in Databricks?

  1. Providing a distributed processing engine
  2. Managing DNS records
  3. Replacing object storage
  4. Providing an email service

Answer: A) Providing a distributed processing engine

Explanation:

Databricks is built on Apache Spark, which provides distributed processing capabilities for large-scale data workloads.

21. Which Databricks feature is designed to incrementally ingest new files arriving in cloud object storage?

  1. Auto Loader
  2. Unity Catalog
  3. SQL Dashboard
  4. Table History

Answer: A) Auto Loader

Explanation:

Auto Loader incrementally processes new files as they arrive in supported cloud storage and uses the Structured Streaming source named cloudFiles.

22. Which Structured Streaming source identifier is used by Auto Loader?

  1. cloudFiles
  2. deltaFiles
  3. autoStream
  4. cloudStream

Answer: A) cloudFiles

Explanation:

Auto Loader uses cloudFiles as its Structured Streaming source format.

23. Which cloud storage systems can Auto Loader process?

  1. Amazon S3, Azure Data Lake Storage, and Google Cloud Storage
  2. Only local hard drives
  3. Only MySQL
  4. Only PostgreSQL

Answer: A) Amazon S3, Azure Data Lake Storage, and Google Cloud Storage

Explanation:

Auto Loader supports cloud object storage sources including Amazon S3, Azure Data Lake Storage, and Google Cloud Storage.

24. What is Structured Streaming used for in Databricks?

  1. Incremental and near-real-time data processing
  2. Static image editing
  3. Operating system installation
  4. Database password generation

Answer: A) Incremental and near-real-time data processing

Explanation:

Spark Structured Streaming enables incremental processing of continuously arriving data using Spark APIs.

25. Which method is commonly used to create a streaming DataFrame in Spark?

  1. readStream
  2. readBatch
  3. streamTable
  4. createStreamFile

Answer: A) readStream

Explanation:

Spark Structured Streaming uses readStream to define streaming sources.

26. Which method is commonly used to write a Spark streaming DataFrame?

  1. writeStream
  2. writeLive
  3. streamWrite
  4. writeContinuousTable

Answer: A) writeStream

Explanation:

The writeStream API is used to configure a Structured Streaming sink.

27. What is a checkpoint used for in Structured Streaming?

  1. Persisting streaming state and progress information
  2. Creating SQL dashboards
  3. Storing Unity Catalog permissions
  4. Compiling notebooks

Answer: A) Persisting streaming state and progress information

Explanation:

Checkpointing allows streaming queries to persist progress and state so processing can recover after failures.

28. What is a major advantage of using Delta Lake with Structured Streaming?

  1. Transactional and reliable streaming reads and writes
  2. It eliminates the need for storage
  3. It converts all streams to SQL automatically
  4. It disables concurrent workloads

Answer: A) Transactional and reliable streaming reads and writes

Explanation:

Delta Lake integrates with Structured Streaming and provides transaction-log-based guarantees for streaming sources and sinks.

29. What is the Bronze layer in a common Databricks medallion architecture?

  1. A raw or minimally processed data layer
  2. A final presentation-only layer
  3. A machine learning model registry
  4. A compute cluster

Answer: A) A raw or minimally processed data layer

Explanation:

The Bronze layer generally contains raw or minimally processed ingested data and serves as the initial layer in a medallion architecture.

30. What is the Silver layer commonly used for?

  1. Cleaned, validated, and transformed data
  2. Raw network packets only
  3. Cluster configuration
  4. Notebook source code only

Answer: A) Cleaned, validated, and transformed data

Explanation:

The Silver layer generally contains refined data after cleansing, validation, normalization, deduplication, or other transformations.

31. What is the Gold layer commonly designed to contain?

  1. Curated data prepared for analytics and business use
  2. Only raw ingestion files
  3. Compute logs only
  4. Temporary checkpoint files

Answer: A) Curated data prepared for analytics and business use

Explanation:

The Gold layer typically contains highly curated datasets optimized for business intelligence, reporting, analytics, or downstream applications.

32. What is the main purpose of medallion architecture?

  1. To progressively refine and enrich data through logical layers
  2. To replace all cloud storage
  3. To eliminate data transformations
  4. To store only machine learning models

Answer: A) To progressively refine and enrich data through logical layers

Explanation:

Medallion architecture organizes data into progressively refined layers, commonly Bronze, Silver, and Gold.

33. Which Databricks feature is used to orchestrate tasks and workflows?

  1. Lakeflow Jobs
  2. Delta History
  3. Unity Catalog Tables
  4. Auto Loader only

Answer: A) Lakeflow Jobs

Explanation:

Lakeflow Jobs can orchestrate workflows consisting of one or more tasks, including notebook-based tasks.

34. What can a Databricks job contain?

  1. One or more tasks with dependencies and execution settings
  2. Only SQL table definitions
  3. Only Delta transaction logs
  4. Only Unity Catalog permissions

Answer: A) One or more tasks with dependencies and execution settings

Explanation:

Jobs can coordinate multiple tasks and define their execution relationships and scheduling or triggering behavior.

35. Which feature can be used to schedule a Databricks workflow?

  1. Lakeflow Jobs scheduling
  2. Delta schema enforcement
  3. Unity Catalog lineage
  4. Auto Loader schema inference only

Answer: A) Lakeflow Jobs scheduling

Explanation:

Lakeflow Jobs supports automated execution of workflows, including scheduled jobs.

36. What is schema enforcement in Delta Lake?

  1. Validating that data written to a table conforms to its schema
  2. Encrypting table files
  3. Scheduling SQL queries
  4. Creating dashboards

Answer: A) Validating that data written to a table conforms to its schema

Explanation:

Delta Lake can enforce table schema requirements when data is written, helping prevent incompatible data from being added.

37. Which SQL statement can be used to create a Delta table?

  1. CREATE TABLE
  2. CREATE DELTA ONLY
  3. NEW DELTA TABLE
  4. BUILD TABLE DELTA

Answer: A) CREATE TABLE

Explanation:

Databricks SQL supports standard CREATE TABLE syntax for creating tables, with Delta Lake used as the default table format unless another format is specified.

38. Which command is commonly used to update existing rows in a Delta table?

  1. UPDATE
  2. CHANGE ROW
  3. MODIFY RECORD ONLY
  4. ALTER DATA VALUE

Answer: A) UPDATE

Explanation:

Delta Lake supports SQL UPDATE operations for modifying rows that satisfy specified conditions.

39. Which command can remove matching rows from a Delta table?

  1. DELETE
  2. REMOVE TABLE ROWS ONLY
  3. DROP ROW DATA
  4. ERASE RECORD

Answer: A) DELETE

Explanation:

Delta Lake supports DELETE operations that remove rows matching a specified condition.

40. What is MERGE commonly used for in Delta Lake?

  1. Combining insert and update logic based on matching conditions
  2. Creating compute clusters
  3. Managing user passwords
  4. Scheduling notebooks

Answer: A) Combining insert and update logic based on matching conditions

Explanation:

MERGE supports conditional insert, update, and delete operations and is commonly used for upserts and change-data-processing workflows.

41. What does upsert generally mean?

  1. Update an existing record or insert it if it does not exist
  2. Delete all records
  3. Only create a new table
  4. Only read existing records

Answer: A) Update an existing record or insert it if it does not exist

Explanation:

An upsert combines update and insert behavior and is commonly implemented with MERGE in Delta Lake.

42. Which feature is commonly used to capture row-level changes from a Delta table?

  1. Change Data Feed
  2. SQL Warehouse History
  3. Notebook Revision Only
  4. Compute Event Log

Answer: A) Change Data Feed

Explanation:

Delta Lake Change Data Feed can track row-level changes between versions of supported tables.

43. Which Databricks capability provides a SQL-based data warehousing experience?

  1. Databricks SQL
  2. Auto Loader
  3. Unity Catalog lineage
  4. Delta transaction log

Answer: A) Databricks SQL

Explanation:

Databricks SQL provides SQL analytics and data warehousing capabilities on the Databricks lakehouse.

44. Which feature can be used to inspect the execution details of a SQL query in Databricks?

  1. Query profile
  2. Unity Catalog schema
  3. Notebook revision
  4. Delta checkpoint

Answer: A) Query profile

Explanation:

Databricks SQL provides query profiling capabilities that help users inspect execution plans and identify performance bottlenecks.

45. Which technology is used by Databricks for distributed query and transformation execution?

  1. Apache Spark
  2. Apache HTTP Server
  3. NGINX
  4. Postfix

Answer: A) Apache Spark

Explanation:

Databricks is built on Apache Spark, which provides the distributed processing foundation for many data transformation and query workloads.

46. Which engine is used alongside Apache Spark in Databricks to accelerate queries and transformations?

  1. Photon
  2. Tomcat
  3. Kafka Connect
  4. ZooKeeper

Answer: A) Photon

Explanation:

Photon is Databricks' native vectorized query engine designed to accelerate SQL and DataFrame workloads on the platform. Databricks reference architectures identify Spark and Photon as engines used for transformations and queries.

47. Which Databricks capability is intended to simplify reliable data processing pipelines?

  1. Lakeflow Declarative Pipelines
  2. Delta History
  3. SQL Editor only
  4. Workspace Folders

Answer: A) Lakeflow Declarative Pipelines

Explanation:

Lakeflow Declarative Pipelines provides a declarative approach for building and managing data processing pipelines, with Databricks handling infrastructure and pipeline dependencies.

48. A company receives millions of JSON files in cloud storage every day and wants to process only newly arrived files incrementally. Which Databricks feature is most directly suited to this requirement?

  1. Auto Loader
  2. Unity Catalog lineage
  3. SQL dashboard
  4. Delta time travel

Answer: A) Auto Loader

Explanation:

Auto Loader is specifically designed for incremental file ingestion from cloud object storage and can scale to very large numbers of files.

49. A streaming job reads from a Delta table, transforms the records, and writes them to another Delta table. Which APIs are central to this workflow?

  1. readStream and writeStream
  2. readHTML and writeHTML
  3. readDeltaOnly and writeDeltaOnly
  4. streamRead and streamSave

Answer: A) readStream and writeStream

Explanation:

Spark Structured Streaming uses readStream for streaming sources and writeStream for streaming sinks. Delta Lake supports both roles in streaming workloads.

50. A Databricks pipeline ingests raw data with Auto Loader, stores it in Bronze Delta tables, cleans and transforms it into Silver tables, and produces business-ready Gold tables governed through Unity Catalog. Which architecture does this represent?

  1. A Databricks lakehouse using a medallion architecture
  2. A traditional single-node database architecture
  3. A DNS-based processing architecture
  4. A client-side web application architecture

Answer: A) A Databricks lakehouse using a medallion architecture

Explanation:

This architecture combines incremental ingestion, Delta Lake storage, progressive Bronze-Silver-Gold data refinement, and centralized governance through Unity Catalog, which are core patterns of the Databricks lakehouse.

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