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

Section	Objectives
Topic 1: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 2. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 3. Explain the advantages and disadvantages of streaming tables compared to materialized views 4. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 5. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 6. Create pipeline components using control flow operators such as if/else and foreach 7. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 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 2: 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 3: Debugging and Deploying	- 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 - Debugging and Troubleshooting 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 3. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
Topic 4: Data Sharing and Federation	- Share and federate data 1. Configure Lakehouse Federation with appropriate governance across supported source systems 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
Topic 5: 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
Topic 6: 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 7: 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 how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 4. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 5. Understand Delta optimization techniques such as deletion vectors and liquid clustering
Topic 8: 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 9: Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Simplify data layout decisions and optimize query performance using liquid clustering 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Design dimensional models for analytical workloads with efficient querying and aggregation
Topic 10: Monitoring and Alerting	- Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use system tables for observability of resource utilization, cost, auditing, and workloads 3. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines - Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q226-Q231):

226. Frage
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 Liquid Clustering and implement a periodic OPTIMIZE command.
B. Alter the table to use Hive-Style Partitions 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.

Antwort: A

Begründung:
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.

227. Frage
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When tables are created, make sure that the external keyword is used in the create table statement.
B. Whenever a table is being created, make sure that the location keyword is used.
C. Whenever a database is being created, make sure that the location keyword is used
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

Antwort: B

Begründung:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

228. Frage
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A. A lightweight single-node cluster with low worker node count to reduce costs.
B. An all-purpose cluster always kept running to ensure low-latency job startup times.
C. A job cluster configured to autoscale across multiple workers during the pipeline run.
D. A high-concurrency cluster designed for interactive SQL workloads.

Antwort: C

Begründung:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.

229. Frage
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
C. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
D. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
E. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

Antwort: B

Begründung:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

230. Frage
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
B. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
C. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
D. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
E. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Antwort: C

Begründung:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed.

231. Frage
......

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