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Exam Review

Key Facts: Databricks Data Engineer Exam

~70%

Est. Pass Rate

Industry estimate

45

Scored Questions

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 scored multiple-choice questions in 90 minutes, plus up to 10 unscored items; Databricks does not publish the passing score. It covers seven sections: the Databricks Intelligence Platform, data ingestion and loading, data transformation and modeling, Lakeflow Jobs, CI/CD, troubleshooting and optimization, and governance and security.

Sample Databricks Data Engineer Practice Questions

Try these sample questions to review concepts for the Databricks Data Engineer exam. Each question includes a detailed explanation. Start the interactive quiz above for the full 194+ 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 Databricks compute type is created for a single job run and is automatically terminated when that run completes?
A.An all-purpose cluster
B.A jobs compute cluster (job cluster)
C.A SQL warehouse
D.A dedicated SQL endpoint
Explanation: A jobs compute cluster (job cluster) is spun up for a specific job run and terminated automatically when the run finishes, making it cheaper than an always-on all-purpose cluster. All-purpose clusters are shared for interactive work and stay running until manually stopped or until their idle auto-termination timeout.
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.
9On Unity Catalog-enabled compute, which access mode lets multiple users securely share a cluster with table access control and data isolation between users?
A.Dedicated (single-user) access mode
B.Standard access mode (formerly High Concurrency / Shared)
C.No-isolation shared access mode
D.Single Node compute
Explanation: Standard access mode (formerly called Shared / High Concurrency) supports multiple concurrent users on the same cluster with user isolation and Unity Catalog table access control. Dedicated (formerly single-user) access mode assigns compute to one user or group; no-isolation mode provides no user isolation.
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 the ability to use the Databricks Platform for foundational data engineering tasks. It covers the platform and its compute options, data ingestion and loading, PySpark and SQL transformation and modeling, Lakeflow Jobs, CI/CD with Git folders and Declarative Automation Bundles, troubleshooting and optimization, and Unity Catalog governance and security.

Exam sponsor: Databricks / Kryterion. The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Questions

45 questions

Time Limit

90 minutes

Passing Score

Not published by Databricks

Exam / Certification Fees

$200

Exam sponsor website

Reported exam pass rate: ~70%. industry estimate This describes exam candidates, not OpenExamPrep users or results from using our resources. Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

6%

Databricks Intelligence Platform

Platform architecture, Delta Lake, Unity Catalog, and choosing compute by workload, limits, and cost

21%

Data Ingestion and Loading

Batch, streaming, and incremental ingestion with COPY INTO, Auto Loader, Lakeflow Connect, and JDBC/ODBC or REST clients

22%

Data Transformation and Modeling

PySpark/SQL cleaning, joins, deduplication, aggregation, basic tuning parameters, and Silver and Gold layer objects

16%

Working with Lakeflow Jobs

Task types and dependencies, retries, conditional tasks, schedules, and file arrival and table update triggers

10%

Implementing CI/CD

Git folders, Declarative Automation Bundles with dev/test/prod targets, and the Databricks CLI

10%

Troubleshooting, Monitoring, and Optimization

Job run history and statuses, Spark UI skew, shuffle, and spill diagnosis, Liquid Clustering, and predictive optimization

15%

Governance and Security

Managed vs external tables, GRANT, REVOKE, and DENY, row filters, column masks, and Unity Catalog ABAC policies

Preparing for the Databricks Data Engineer Exam

What You Need to Know

  • Passing score: Not published by Databricks
  • Exam length: 45 questions
  • Time limit: 90 minutes
  • Exam / certification fees: $200 Official sources

Using Our Practice Resources

  • Work through all 194 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

Databricks Data Engineer: Suggested Study Strategy

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
5Practice Lakeflow Jobs: task dependencies, retries, conditional tasks, and scheduled, file arrival, and table update triggers

Frequently Asked Questions

What is the Databricks Data Engineer exam pass rate?

Databricks does not publish a pass rate or a fixed passing score; its FAQ says passing scores are set statistically and can change as exams are updated. The exam has 45 scored multiple-choice questions in 90 minutes, and up to 10 unscored items may also appear.

What Databricks features should I focus on?

Key areas in the August 2026 guide: COPY INTO, Auto Loader, and Lakeflow Connect ingestion; PySpark and SQL joins and transformations; bronze, silver, and gold modeling; Lakeflow Jobs tasks, retries, and triggers; Git folders, Declarative Automation Bundles, and the Databricks CLI; Spark UI skew and spill diagnosis; and Unity Catalog grants, row filters, column masks, and ABAC.

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.