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

Section	Objectives
Topic 1: Ensuring Data Security and Compliance	- Data Security 1. Use row filters and column masks for sensitive data 2. Apply anonymization and pseudonymization techniques 3. Use ACLs to secure workspace objects and enforce least privilege - Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information
Topic 2: Data Transformation, Cleansing, and Quality	- Data Quality 1. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader - Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets
Topic 3: Cost & Performance Optimisation	- Delta Optimization 1. Use Change Data Feed to address streaming table limitations and improve latency 2. Apply data skipping and file pruning techniques 3. Understand deletion vectors and liquid clustering - Query Performance 1. Identify inefficient joins and excessive data shuffling 2. Use Query Profile to identify performance bottlenecks - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead
Topic 4: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDFs 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Manage and troubleshoot third-party library installations and dependencies - Building and Testing ETL Pipelines 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 2. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 4. Configure environments, dependencies, memory, and retry behavior 5. Develop unit and integration tests for data processing code 6. Use control flow operators in pipeline components 7. Use APPLY CHANGES APIs for change data capture 8. Compare streaming tables and materialized views
Topic 5: Debugging and Deploying	- Deploying CI/CD 1. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles - Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Analyze errors and remediate failed job runs
Topic 6: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Design and implement scalable data models using Delta Lake 3. Understand Liquid Clustering versus partitioning and Z-Ordering
Topic 7: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 2. Use Query Profiler and Spark UI to monitor workloads 3. Use system tables for resource, cost, audit, and workload monitoring 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
Topic 8: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Build append-only pipelines for batch and streaming data using Delta 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Ingest data from message buses and cloud storage
Topic 9: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Share live Lakehouse data with external computing platforms 2. Configure sharing with external platforms using the open sharing protocol 3. Configure Databricks-to-Databricks Sharing
Topic 10: Data Governance	- Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model - Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q229-Q234):

229. Frage
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
B. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
C. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
D. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.

Antwort: B

Begründung:
The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.

230. Frage
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?

A. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
B. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
C. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
D. Require the analytics team to use a tool that supports Delta table.

Antwort: B

Begründung:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.

231. Frage
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

Antwort: C

Begründung:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

232. Frage
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
B. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
C. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
D. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.

Antwort: C

Begründung:
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

233. Frage
When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A. In the Query Profiler, under Query Source
B. In the Query Profiler, under the Stages tab
C. In the Spark UI, under the SQL/DataFrame tab
D. In the Spart UI, under the Jobs tab

Antwort: C

Begründung:
The Spark UI provides detailed visibility into how queries are executed. The SQL/DataFrame tab displays the logical and physical query plans, allowing engineers to visualize execution details and understand how the workload is processed across stages and operators.

234. Frage
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