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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q199-Q204):

NEW QUESTION # 199
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
C. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
E. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

Answer: E

Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly. Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB. The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).

NEW QUESTION # 200
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. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
B. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
C. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
D. Require the analytics team to use a tool that supports Delta table.

Answer: B

Explanation:
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.

NEW QUESTION # 201
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
B. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
C. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.

Answer: D

Explanation:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

NEW QUESTION # 202
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When a database is being created, make sure that the LOCATION keyword is used.
B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
C. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
E. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

Answer: C

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

NEW QUESTION # 203
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. Enable deletion vectors on the table if not already enabled.
C. Partition the table by date.
D. Apply liquid clustering using the merge join keys.
E. Use ZORDER on high-cardinality columns.

Answer: B,D

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 # 204
......

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