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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
Topic 2: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
Topic 3: Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Design dimensional models for analytical workloads with efficient querying and aggregation 3. Identify the benefits of liquid clustering over partitioning and Z-Ordering 4. Design and implement scalable data models using Delta Lake to manage large datasets
Topic 4: Data Governance	- Govern enterprise data 1. Demonstrate understanding of the Unity Catalog permission inheritance model 2. Create and add descriptions and metadata to enterprise data to improve discoverability
Topic 5: Data Sharing and Federation	- Share and federate data 1. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 2. Configure Lakehouse Federation with appropriate governance across supported source systems 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
Topic 6: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Explain the advantages and disadvantages of streaming tables compared to materialized views 2. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 3. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 4. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 5. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 7. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 8. Create pipeline components using control flow operators such as if/else and foreach - Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Develop User-Defined Functions using Pandas/Python UDF 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
Topic 7: Monitoring and Alerting	- Monitoring 1. Use system tables for observability of resource utilization, cost, auditing, and workloads 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 3. Use Query Profile and Spark UI to monitor workloads 4. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines - Alerting 1. Use SQL Alerts to monitor data quality 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
Topic 8: Cost & Performance Optimization	- Optimize cost and performance 1. Apply Change Data Feed to address streaming table limitations and improve latency 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 4. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 5. Understand Delta optimization techniques such as deletion vectors and liquid clustering
Topic 9: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors - Deploying CI/CD 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment 2. Build and deploy Databricks resources using Databricks Asset Bundles
Topic 10: Ensuring Data Security and Compliance	- Applying Data Security Mechanisms 1. Use ACLs to secure workspace objects and enforce the principle of least privilege 2. Use row filters and column masks to protect sensitive table data 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization - Ensuring Compliance 1. Implement compliant batch and streaming pipelines that detect and mask PII 2. Develop data purging solutions that comply with data retention policies

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Databricks Certified Data Engineer Professional Sample Questions (Q221-Q226):

NEW QUESTION # 221
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
C. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
D. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")

Answer: B

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 222
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
B. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
C. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
D. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

Answer: B

Explanation:
Databricks recommends Liquid Clustering for optimizing data layout in large Delta tables where query filters involve high-cardinality columns. Liquid Clustering automatically manages file organization and supports incremental maintenance without the need to rewrite data when clustering keys evolve. This is a key advantage over static partitioning or Z-ordering, which require costly file rewrites whenever optimization keys change. By combining Liquid Clustering with a periodic OPTIMIZE command, Databricks automatically compacts small files and maintains efficient data skipping performance. As stated in the Delta Lake optimization guide, Liquid Clustering is designed for scalability, minimal maintenance, and adaptability for analytical workloads with evolving query patterns--making B the correct answer.

NEW QUESTION # 223
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
E. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Answer: B

NEW QUESTION # 224
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Overwrite the table instead of Merge.
B. Use ZORDER on high-cardinality columns.
C. Partition the table by date.
D. Enable deletion vectors on the table if not already enabled.
E. Apply liquid clustering using the merge join keys.

Answer: D,E

Explanation:
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

NEW QUESTION # 225
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
B. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
C. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
D. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.

Answer: A

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

NEW QUESTION # 226
......

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