Free Practice Questions for Databricks Certified Data Engineer Professional - October 9 onwards Certification

Subdomain 1.1: Implement scalable Python project structures for Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs) to support modular development and CI/CD integration.

Subdomain 1.2: Apply troubleshooting techniques to dependency conflicts and installation failures for external libraries (PyPI, local wheels, source archives) across serverless, pipeline, and bundle-deployed environments.

Subdomain 1.3: Develop User-Defined Functions (UDFs) using Pandas, Python, and SQL, including Unity Catalog functions for the given constraints.

Subdomain 1.4: Create production-ready data pipelines for streaming data using Lakeflow Declarative Pipelines and Auto Loader.

Subdomain 1.5: Create and automate ETL workloads using Lakeflow Jobs via the UI, API, and CLI.

Subdomain 1.6: Choose between a streaming table and a materialized view for a given latency, cost, and refresh requirement.

Subdomain 1.7: 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.

Subdomain 1.8: Choose between Spark Structured Streaming and Apache Spark™ Declarative Pipelines for scalable ETL given operational constraints.

Subdomain 1.9: Create Lakeflow Jobs that use control-flow operators such as If/Else conditions and For Each loops.

Subdomain 1.10: 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).

Subdomain 1.11: Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, and testing frameworks to ensure code correctness.

Subdomain 1.12: Apply Structured Streaming stateful-processing semantics, including watermarks, output modes, foreachBatch, and checkpoints for fault-tolerant exactly-once state recovery.

Subdomain 2.1: 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.

Subdomain 2.2: Configure incremental CDC pipelines using Lakeflow Pipelines with Delta or Iceberg as the target table format.

Subdomain 2.3: Configure CDC ingestion pipelines from relational database sources, including SQL Server, MySQL, and PostgreSQL, using Lakeflow Connect.

Subdomain 2.4: Implement OpenSharing (D2D and Databricks-to-Open) and Clean Rooms for privacy-preserving collaboration.

Subdomain 2.5: Configure Lakehouse Federation with governance across supported source systems, applying UC permissions and connection-level credentials.

Subdomain 3.1: Apply advanced data transformations, including window functions, joins, and aggregations, using Spark SQL and PySpark to process large datasets.

Subdomain 3.2: Develop a model and query semi-structured data using the VARIANT data type and related functions (e.g., parse_json, variant_get).

Subdomain 3.3: Apply AI functions, including ai_query, to perform model inference within data pipelines for enrichment and classification tasks.

Subdomain 3.4: Implement data quality expectations in Lakeflow Declarative Pipelines to quarantine, drop, or fail on bad records.

Subdomain 4.1: Use Databricks system tables (billing, compute, access, lakeflow) for cost analysis, auditing, and workload monitoring.

Subdomain 4.2: Use Databricks REST APIs / CLI and SDK for monitoring and analyzing jobs and pipelines.

Subdomain 4.3: Use Apache Spark Declarative Pipelines event logs to monitor pipeline health and data quality.

Subdomain 4.4: 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.

Subdomain 4.5: Use Lakeflow Jobs UI and Jobs API to monitor job status and performance metrics.

Subdomain 5.1: Explain how Unity Catalog managed tables, Predictive Optimization, and Liquid Clustering reduce operational and maintenance overhead for a given workload.

Subdomain 5.2: Choose the appropriate Delta optimization technique (deletion vectors, Liquid Clustering, CLUSTER BY AUTO) for a given table access pattern.

Subdomain 5.3: Understand how caching (Delta cache) improves query performance for repeated reads of the same data.

Subdomain 5.4: Apply Change Data Feed (CDF) to expose row-level changes (updates/deletes) for efficient incremental downstream processing.

Subdomain 5.5: Use the query profile to identify performance bottlenecks, such as data-skipping inefficiencies, join strategies, and shuffle operations.

Subdomain 5.6: Compare Liquid Clustering vs partitioning/ZORDER for a given table size and query pattern.

Subdomain 6.1: Apply least-privilege access control lists (ACLs) to secure Unity Catalog securable objects and workspace resources.

Subdomain 6.2: Apply attribute-based access control (ABAC) policies with governed tags to enforce row filters and column masks at scale.

Subdomain 6.3: 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).

Subdomain 6.4: Implement a compliant data pipeline that enforces PII detection and masking controls across batch and streaming workloads using Unity Catalog features.

Subdomain 6.5: 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.

Subdomain 7.1: Demonstrate understanding of Unity Catalog tags and comments as mechanisms for adding metadata to securable objects to improve data discoverability.

Subdomain 7.2: Demonstrate understanding of the Unity Catalog permission inheritance model as a mechanism for managing access control across catalogs, schemas, and objects.

Subdomain 8.1: Identify pertinent diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors.

Subdomain 8.2: Analyze the errors and remediate the failed job runs with job repairs and parameter overrides.

Subdomain 8.3: Use event logs and the Spark UI to debug pipelines and Spark workloads.

Subdomain 8.4: Deploy Databricks resources using Declarative Automation Bundles (formerly Databricks Asset Bundles).

Subdomain 8.5: Configure Git-based CI/CD workflows using Databricks Git folders (formerly Repos) to deploy notebooks and code.

Subdomain 9.1: 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.

Subdomain 9.2: 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.