Practice Exams: Databricks Data Engineer Professional

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

There are 70 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 (23%)

• Implement scalable Python project structures for Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs) to support modular development and CI/CD integration.

• Apply troubleshooting techniques to dependency conflicts and installation failures for external libraries (PyPI, local wheels, source archives) across serverless, pipeline, and bundle-deployed environments.

• Develop User-Defined Functions (UDFs) using Pandas, Python, and SQL, including Unity Catalog functions for the given constraints.

• Create production-ready data pipelines for streaming data using Lakeflow Declarative Pipelines and Auto Loader.

• Create and automate ETL workloads using Lakeflow Jobs via the UI, API, and CLI.

• Choose between a streaming table and a materialized view for a given latency, cost, and refresh requirement.

• Implement CDC pipelines using AUTO CDC APIs (formerly APPLY CHANGES) in Lakeflow Declarative Pipelines, including SCD Type 1 and Type 2 via stored_as_scd_type.

• Choose between Spark Structured Streaming and Apache Spark™ Declarative Pipelines for scalable ETL given operational constraints.

• Create Lakeflow Jobs that use control-flow operators such as If/Else conditions and For Each loops.

• Choose appropriate compute and configuration for environments and dependencies — including serverless compute (serverless environments, dependency management, performance mode), high-memory notebook tasks, and auto-optimization settings (e.g., disallowing retries).

• Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual,

• DataFrame.transform, and testing frameworks to ensure code correctness.

• Apply Structured Streaming stateful-processing semantics, including watermarks, output modes, foreachBatch, and checkpoints for fault-tolerant exactly-once state recovery

Section 2: Data Ingestion & Acquisition (12%)

• Develop data ingestion pipelines to ingest a variety of data formats, including Delta Lake, Parquet, JSON, Iceberg, CSV, and Binary from diverse sources such as message buses (Kafka, Kinesis, Pub/Sub) and cloud storage.

• Configure incremental CDC pipelines using Lakeflow Pipelines with Delta or Iceberg as the target table format.

• Configure CDC ingestion pipelines from relational database sources, including SQL Server, MySQL, and PostgreSQL, using Lakeflow Connect.

• Implement OpenSharing (D2D and Databricks-to-Open) and Clean Rooms for privacy-preserving collaboration.

• Configure Lakehouse Federation with governance across supported source systems, applying UC permissions and connection-level credentials.

Section 3: Data Manipulation (12%)

• Apply advanced data transformations, including window functions, joins, and aggregations, using Spark SQL and PySpark to process large datasets.

• Develop a model and query semi-structured data using the VARIANT data type and related functions (e.g., parse_json, variant_get).

• Apply AI functions, including ai_query, to perform model inference within data pipelines for enrichment and classification tasks.

• Implement data quality expectations in Lakeflow Declarative Pipelines to quarantine, drop, or fail on bad records.

Section 4: Monitoring and Alerting (10%)

• Use Databricks system tables (billing, compute, access, lakeflow) for cost analysis, auditing, and workload monitoring.

• Use Databricks REST APIs / CLI and SDK for monitoring and analyzing jobs and pipelines.

• Use Apache Spark Declarative Pipelines event logs to monitor pipeline health and data quality.

• Use Databricks Lakehouse alerts to monitor governed business metrics, data quality and anomaly signals, usage and cost, SQL warehouse/query health, audit and security events, AI agent quality, and Lakeflow Job branching.

• Use Lakeflow Jobs UI and Jobs API to monitor job status and performance metrics

Section 5: Cost & Performance Optimization(15%)

• Explain how Unity Catalog managed tables, Predictive Optimization, and Liquid Clustering reduce operational and maintenance overhead for a given workload.

• Choose the appropriate Delta optimization technique (deletion vectors, Liquid Clustering, CLUSTER BY AUTO) for a given table access pattern.

• Understand how caching (Delta cache) improves query performance for repeated reads of the same data.

• Apply Change Data Feed (CDF) to expose row-level changes (updates/deletes) for efficient incremental downstream processing.

• Use the query profile to identify performance bottlenecks, such as data-skipping inefficiencies, join strategies, and shuffle operations.

• Compare Liquid Clustering vs partitioning/ZORDER for a given table size and query pattern.

Section 6: Ensuring Data Security and Compliance (8%)

• Apply least-privilege access control lists (ACLs) to secure Unity Catalog securable objects and workspace resources.

• Apply attribute-based access control (ABAC) policies with governed tags to enforce row filters and column masks at scale.

• Apply anonymization and pseudonymization methods — such as hashing, tokenization, suppression, and generalization — to confidential data (e.g., using column masks and related Unity Catalog features).

• Implement a compliant data pipeline that enforces PII detection and masking controls across batch and streaming workloads using Unity Catalog features.

• Implement a data purging strategy that satisfies data retention and deletion policies (e.g., GDPR right-to-erasure, GDPR right-to-be-forgotten deletion) using Delta Lake and Unity Catalog features.

Section 7: Data Governance (5%)

• Demonstrate understanding of Unity Catalog tags and comments as mechanisms for adding metadata to securable objects to improve data discoverability.

• Demonstrate understanding of the Unity Catalog permission inheritance model as a mechanism for managing access control across catalogs, schemas, and objects.

Section 8: Debugging and Deploying (10%)

• 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 event logs and the Spark UI to debug pipelines and Spark workloads.

• Deploy Databricks resources using Declarative Automation Bundles (formerly Databricks Asset Bundles).

• Configure Git-based CI/CD workflows using Databricks Git folders (formerly Repos) to deploy notebooks and code.

Section 9: Data Modeling (5%)

● Design scalable Delta/Iceberg table layouts for large data assets by mapping partitioning to data grain, aligning clustering to relationship access patterns, and maintaining balanced file sizes through compaction.

● Design dimensional models for analytical workloads, leveraging Materialized Views for pre-computed aggregation and Unity Catalog Metric Views for governed, reusable metric definitions, to ensure 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!