Practice Exams: Databricks Data Engineer Professional

NEW: V30 Sep 2025 | Practice Tests with Detailed Explanations for Databricks Certified Data Engineer Professional exam

If you looking for practice tests for Databricks Data Engineer Professional certification exam, you have come to the right place! Two practice tests with detailed explanations are available to prepare you before appearing for the actual exam

There are 59 multiple-choice questions on each practice exam. The questions will be distributed by high-level topic in the following way:

1. Developing Code for Data Processing using Python and SQL – 22%

2. Data Ingestion & Acquisition – 7%

3. Data Transformation, Cleansing, and Quality – 10%

4. Data Sharing and Federation – 5%

5. Monitoring and Alerting – 10%

6. Cost & Performance Optimisation – 13%

7. Ensuring Data Security and Compliance – 10%

8. Data Governance – 7%

9. Debugging and Deploying – 10%

10. Data Modelling – 6%

Duration

Testers will have 120 minutes to complete each practice exam.

Exam outline

The practice tests cover the following exam topics with detailed explanations:

Section 1: Developing Code for Data Processing using Python and SQL

• Using Python and Tools for development

  • Design and implement a scalable Python project structure optimized for Databricks Asset Bundles (DABs), enabling modular development, deployment automation, and CI/CD integration.

  • Manage and troubleshoot external third-party library installations and dependencies in Databricks, including PyPI packages, local wheels, and source archives.

  • Develop User-Defined Functions (UDFs) using Pandas/Python UDF

• Building and Testing an ETL pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark on the Databricks platform

  • Build and manage reliable, production-ready data pipelines, for batch and streaming data using Lakeflow Declarative Pipelines and Autoloader.

  • Create and Automate ETL workloads using Jobs via UI/APIs/CLI.

  • Explain the advantages and disadvantages of streaming tables compared to materialized views.

  • Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines.

  • Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for building scalable ETL pipelines. ● Create a pipeline component that uses control flow operators (e.g. if/else, foreach, etc.)

  • Choose the appropriate configs for environments and dependencies, high memory for notebook tasks, and auto-optimization to disallow retries.

  • Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, and testing frameworks, to ensure code correctness, including a built-in debugger.

Section 2: Data Ingestion & Acquisition:

• Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.

• Create an append-only data pipeline capable of handling both batch and streaming data using Delta.

Section 3: Data Transformation, Cleansing, and Quality

• Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.

• Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or autoloader in classic jobs.

Section 4: Data Sharing and Federation

• Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing(D2D) or to external platforms using open sharing protocol(D2O).

• Configure Lakehouse Federation with proper governance across supported source Systems.

• Use Delta Share to share live data from Lakehouse to any computing platform.

Section 5: Monitoring and Alerting

• Monitoring

  • Use system tables for observability over resource utilization, cost, auditing and workload monitoring.

  • Use Query Profiler UI and Spark UI to monitor workloads. ● Use the Databricks REST APIs/Databricks CLI for monitoring jobs and pipelines.

  • Use Lakeflow Declarative Pipelines Event Logs to monitor pipelines.

• Alerting

  • Use SQL Alerts to monitor data quality.

  • Use the Workflows UI and Jobs API to set up job status and performance issue notifications.

Section 6: Cost & Performance Optimisation

• Understand how / why using Unity Catalog managed tables reduces operation Overhead and maintenance burden.

• Understand delta optimization techniques, such as deletion vectors and liquid clustering.

• Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc).

• Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.

• Use query profile to analyze the query and identify bottlenecks, such as bad data kipping, inefficient types of joins, data shuffling.

Section 7: Ensuring Data Security and Compliance

• Applying Data Security mechanisms.

  • Use ACLs to secure Workspace Objects, enforcing the principle of least privilege, including enforcing principles like least privilege, policy enforcement.

  • Use row filters and column masks to filter and mask sensitive table data.

  • Apply anonymization and pseudonymization methods such as Hashing, Tokenization, Suppression, and Generalization to confidential data.

• Ensuring Compliance

  • Implement a compliant batch & streaming pipeline that detects and applies masking of PII to ensure data privacy.

  • Develop a data purging solution ensuring compliance with data retention policies.

Section 8: Data Governance

• Create and add descriptions/metadata about enterprise data to make it more discoverable.

• Demonstrate understanding of Unity Catalog permission inheritance model.

Section 9: Debugging and Deploying

• Debugging and Troubleshooting

  • Identify pertinent diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors.

  • Analyze the errors and remediate the failed job runs with job repairs and parameter overrides.

  • Use Lakeflow Declarative Pipelines event logs & the Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines.

• Deploying CI/CD

  • Build and Deploy Databricks resources using Databricks Asset Bundles.

  • Configure and integrate with Git-based CI/CD workflows using Databricks Git Folders for notebook and code deployment.

Section 10: Data Modelling

• Design and implement scalable data models using Delta Lake to manage large datasets.

• Simplify data layout decisions and optimize query performance using Liquid Clustering.

• Identify the benefits of using liquid Clustering over Partitioning and ZOrder.

• Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.

After passing these practice exams, you will be ready to take the certification exam.

I wish you all the best in your exam!