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200+ Free Databricks Data Engineer Practice Questions

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Question 1
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What is the primary difference between the Databricks Control Plane and Data Plane?

A
B
C
D
to track
2026 Statistics

Key Facts: Databricks Data Engineer Exam

~70%

Est. Pass Rate

Industry estimate

70%

Passing Score

Databricks

40-60 hrs

Study Time

Recommended

90 min

Exam Duration

Databricks

$200

Exam Fee

Databricks

2 years

Cert Valid

Databricks

The Databricks Data Engineer Associate exam has 45 questions in 90 minutes, requiring 70% to pass. The exam covers Databricks Lakehouse Platform, ELT with Spark SQL and Python, Delta Lake, incremental data processing, data pipelines, and data governance.

Sample Databricks Data Engineer Practice Questions

Try these sample questions to test your Databricks Data Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.

1What is the primary difference between the Databricks Control Plane and Data Plane?
A.The Control Plane manages compute resources while the Data Plane handles user authentication
B.The Control Plane hosts the Databricks web application and manages jobs/notebooks, while the Data Plane processes customer data in the customer's cloud account
C.The Control Plane stores all customer data while the Data Plane manages network security
D.The Control Plane is optional while the Data Plane is required for all operations
Explanation: The Control Plane hosts the Databricks web application, manages notebooks, jobs, and cluster management APIs. It does not process customer data. The Data Plane is where actual data processing occurs, residing in the customer's cloud account (VPC/VNet) for security and compliance. This separation ensures customer data never leaves their cloud environment during processing.
2What is a Delta Lake?
A.A proprietary cloud storage service from Databricks
B.An open-source storage layer that brings ACID transactions to Apache Spark and big data workloads
C.A real-time streaming processing engine
D.A machine learning model management system
Explanation: Delta Lake is an open-source storage layer that runs on top of existing data lakes (like S3, ADLS, GCS). It provides ACID transactions, scalable metadata handling, and unified streaming/batch data processing. Delta Lake is not proprietary to Databricks and is a Linux Foundation project.
3Which cluster mode in Databricks terminates automatically when not in use?
A.All-Purpose Cluster
B.Job Cluster
C.SQL Warehouse
D.High Concurrency Cluster
Explanation: Job Clusters are created specifically for running a job and terminate automatically when the job completes. All-Purpose Clusters remain running until manually terminated. SQL Warehouses (formerly SQL Endpoints) can be configured with auto-stop but are designed for interactive SQL queries. High Concurrency Clusters are for sharing among multiple users with table ACLs.
4What does the Databricks File System (DBFS) provide?
A.A distributed SQL query engine
B.A layer of abstraction over cloud object storage that allows mounting various storage locations
C.A machine learning model registry
D.A real-time data ingestion service
Explanation: DBFS (Databricks File System) is a layer of abstraction over cloud object storage (S3, ADLS, GCS) that provides a filesystem-like interface. It allows mounting external storage locations and provides a consistent path structure (/dbfs/...) for accessing data across the workspace. DBFS is not a separate storage system but an abstraction layer.
5A data engineer needs to share notebooks and code with team members while maintaining version history. Which Databricks feature should they use?
A.DBFS mounts
B.Databricks Repos
C.Delta Lake time travel
D.Unity Catalog
Explanation: Databricks Repos provides integration with Git providers (GitHub, GitLab, Azure DevOps, Bitbucket) allowing users to version control notebooks and code files. It enables branching, committing, pulling, and pushing changes to remote repositories. DBFS is for storage, Delta Lake time travel is for data versioning, and Unity Catalog is for data governance.
6Which dbutils command is used to list files in a DBFS directory?
A.dbutils.fs.mount()
B.dbutils.fs.ls()
C.dbutils.fs.cp()
D.dbutils.fs.mkdirs()
Explanation: dbutils.fs.ls("/path/to/directory") lists the contents of a directory in DBFS. The mount() command mounts external storage, cp() copies files, and mkdirs() creates directories. The dbutils.fs module provides filesystem-like utilities for working with DBFS and mounted storage.
7What is the main advantage of using Delta Lake over standard Parquet files in a data lake?
A.Delta Lake files are smaller than Parquet files
B.Delta Lake provides ACID transactions, time travel, and automatic schema evolution
C.Delta Lake can only be used with Python
D.Delta Lake requires less storage space
Explanation: Delta Lake builds on Parquet format but adds critical enterprise features: ACID transactions (Atomicity, Consistency, Isolation, Durability), time travel (query previous versions), automatic schema evolution/validation, and optimized performance features like Z-ordering and data skipping. The files are still Parquet-based but with transaction logs.
8In Databricks, what is the purpose of a Service Principal?
A.To provide interactive notebook access for human users
B.To enable automated processes and applications to authenticate and access Databricks resources securely
C.To monitor cluster performance metrics
D.To manage SQL warehouse queries
Explanation: Service Principals are security identities used by applications, services, or automation tools to access Databricks resources. Unlike user accounts, they are designed for non-interactive/automated scenarios like CI/CD pipelines, scheduled jobs, and application integrations. They can be assigned permissions and used with token-based authentication.
9Which cluster mode should be used when multiple users need to share a cluster with fine-grained table access control?
A.Standard cluster mode
B.High Concurrency cluster mode with table access control enabled
C.Single Node cluster mode
D.Job cluster mode
Explanation: High Concurrency clusters are optimized for sharing among multiple users and support fine-grained table access controls (ACLs) using Unity Catalog or legacy table ACLs. They use preemption to ensure fair resource sharing and support credential passthrough. Standard clusters are for single users or shared scenarios without ACL requirements.
10What happens when you run the VACUUM command on a Delta table?
A.It optimizes the table layout for faster queries
B.It removes old files that are no longer needed for time travel, beyond the retention period
C.It repairs the table by recreating missing files
D.It exports the table data to external storage
Explanation: VACUUM removes old data files that are no longer referenced by the current table state and are older than the retention period (default 7 days). This helps manage storage costs but means those file versions can no longer be accessed via time travel. VACUUM does not optimize layout (OPTIMIZE does) or repair tables.

About the Databricks Data Engineer Exam

The Databricks Certified Data Engineer Associate validates skills in using Databricks Lakehouse Platform for data engineering tasks. It covers ELT with Spark SQL and Python, Delta Lake, data pipelines, data governance, and production workload management.

Questions

45 scored questions

Time Limit

90 minutes

Passing Score

70%

Exam Fee

$200 (Databricks / Kryterion)

Databricks Data Engineer Exam Content Outline

24%

ELT with Spark SQL & Python

Data extraction, transformation, loading, Spark SQL queries, Python DataFrame operations, and UDFs

22%

Delta Lake & Lakehouse

Delta Lake architecture, ACID transactions, time travel, schema enforcement/evolution, and medallion architecture

22%

Incremental Data Processing

Structured Streaming, Auto Loader, Change Data Feed, and incremental ETL patterns

16%

Production Pipelines

Delta Live Tables, job orchestration, multi-task jobs, error handling, and monitoring

16%

Data Governance

Unity Catalog, access controls, data lineage, data discovery, and compliance

How to Pass the Databricks Data Engineer Exam

What You Need to Know

  • Passing score: 70%
  • Exam length: 45 questions
  • Time limit: 90 minutes
  • Exam fee: $200

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Databricks Data Engineer Study Tips from Top Performers

1Master Delta Lake concepts: ACID transactions, time travel, OPTIMIZE, VACUUM, and schema evolution
2Practice Spark SQL and Python DataFrame operations extensively
3Understand the medallion architecture: bronze (raw), silver (cleaned), gold (aggregated)
4Study Auto Loader for incremental file ingestion and Structured Streaming for real-time data
5Know Delta Live Tables syntax and pipeline management in production

Frequently Asked Questions

What is the Databricks Data Engineer exam pass rate?

The estimated pass rate is approximately 70%. The exam has 45 questions in 90 minutes, requiring 70% to pass. Questions focus on practical Databricks platform usage.

What Databricks features should I focus on?

Key areas: Delta Lake (ACID, time travel, schema evolution), Spark SQL, Auto Loader, Structured Streaming, Delta Live Tables, Unity Catalog, and medallion architecture (bronze/silver/gold).

Do I need hands-on Databricks experience?

Yes, hands-on experience is strongly recommended. The exam tests practical skills in building data pipelines, using Delta Lake, and managing production workloads on Databricks.

How long should I study?

Most candidates study for 4-6 weeks, investing 40-60 hours. Use the Databricks Academy free courses and hands-on labs as primary study material.