Databricks · Professional
Databricks Data Engineer Professional
Demonstrate advanced data engineering on Databricks: production code in Python and SQL, ingestion, transformation and quality, data sharing, monitoring, cost/performance optimization, security, governance, and deployment.
Data Engineer Professional exam at a glance
| Exam code | — |
|---|---|
| Vendor | Databricks |
| Level | Professional |
| Domains | 10 |
| Published practice questions | 311 |
| Our mock exam | 59 questions in 120 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | July 9, 2026 |
| Official exam page | Databricks — Data Engineer Professional |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official Data Engineer Professional exam page for the current format, price and scoring before you book.
Data Engineer Professional exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Developing Code for Data Processing using Python and SQL
22%Writing production-grade PySpark and Spark SQL, using the DataFrame API, UDFs, control flow, and reusable, testable code on Databricks.
- PySpark DataFrame API and Spark SQL for transformations
- Reading/writing Delta tables; MERGE, INSERT, UPDATE, DELETE
- User-defined functions (UDFs) and their performance trade-offs
- Structured Streaming for incremental processing
- Parameterizing and modularizing notebooks/jobs (widgets, dbutils)
- Handling schema evolution and enforcement in Delta
Free Developing Code for Data Processing using Python and SQL practice questions →Developing Code for Data Processing using Python and SQL study guide →
Data Ingestion and Acquisition
7%Ingesting batch and streaming data into the Lakehouse using Auto Loader, COPY INTO, and streaming sources.
- Auto Loader (cloudFiles) for incremental file ingestion and schema evolution
- COPY INTO for idempotent batch ingestion
- Streaming ingestion sources and checkpointing
- Ingesting from cloud object storage and external systems
Free Data Ingestion and Acquisition practice questions →Data Ingestion and Acquisition study guide →
Data Transformation, Cleansing, and Quality
10%Transforming and cleansing data and enforcing data quality with Delta Live Tables expectations and the medallion architecture.
- Medallion architecture (bronze/silver/gold)
- Delta Live Tables (DLT) and expectations for data quality
- Deduplication, handling nulls, and standardization
- Change Data Capture (CDC) with APPLY CHANGES INTO
Free Data Transformation, Cleansing, and Quality practice questions →Data Transformation, Cleansing, and Quality study guide →
Data Sharing and Federation
5%Sharing data securely across organizations with Delta Sharing and querying external systems with Lakehouse Federation.
- Delta Sharing for secure cross-organization data sharing
- Unity Catalog shares, recipients, and providers
- Lakehouse Federation to query external databases without ingestion
Free Data Sharing and Federation practice questions →Data Sharing and Federation study guide →
Monitoring and Alerting
10%Monitoring pipeline and job health, streaming metrics, and configuring alerts on Databricks.
- Monitoring Databricks Jobs runs and task status
- Structured Streaming metrics (input/processing rate, batch duration)
- Lakehouse Monitoring for data quality/drift
- Alerts on job failures and SQL query thresholds
Free Monitoring and Alerting practice questions →Monitoring and Alerting study guide →
Cost and Performance Optimization
13%Optimizing query and pipeline performance and controlling cost through Delta layout, cluster sizing, and Spark tuning.
- Delta optimization: OPTIMIZE, ZORDER, file compaction, and liquid clustering
- Partitioning strategy and avoiding small files
- Cluster sizing, autoscaling, spot instances, and photon
- Caching, broadcast joins, and skew handling
- VACUUM and managing storage costs
Free Cost and Performance Optimization practice questions →Cost and Performance Optimization study guide →
Ensuring Data Security and Compliance
10%Securing data with access controls, encryption, row/column-level security, and PII handling on the Databricks platform.
- Unity Catalog privileges and securable objects
- Row filters and column masks for fine-grained access control
- Encryption at rest/in transit and secrets management
- Handling PII and compliance requirements (deletion, masking)
Free Ensuring Data Security and Compliance practice questions →Ensuring Data Security and Compliance study guide →
Data Governance
7%Governing the data estate with Unity Catalog: lineage, discovery, tags, and audit.
- Unity Catalog object model (metastore, catalog, schema, table)
- Data lineage and discovery
- Tags and classification for governance
- Audit logs and access history
Free Data Governance practice questions →Data Governance study guide →
Debugging and Deploying
10%Debugging failed pipelines and deploying data engineering workloads with Jobs, CI/CD, and Databricks Asset Bundles.
- Debugging failed Jobs/DLT pipelines using logs and the Spark UI
- Retries, task dependencies, and orchestration in Databricks Jobs (Lakeflow Jobs)
- CI/CD and environment promotion with Databricks Asset Bundles
- Databricks Repos and version control for deployment
Free Debugging and Deploying practice questions →Debugging and Deploying study guide →
Data Modeling
6%Modeling data in the Lakehouse: dimensional models, slowly changing dimensions, and Delta table design.
- Dimensional modeling (facts and dimensions) in the Lakehouse
- Slowly Changing Dimensions (SCD Type 1 and Type 2)
- Delta table design and constraints
- Modeling for the medallion architecture
Free Data Modeling practice questions →Data Modeling study guide →
Data Engineer Professional FAQ
How is the Data Engineer Professional practice assessment configured?
Our practice format targets 59 questions in 120 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in Data Engineer Professional?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study Data Engineer Professional?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use Data Engineer Professional exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official Databricks exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.