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

Section	Objectives
Topic 1: 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. 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. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
Topic 2: Data Governance	- Govern enterprise data 1. Create and add descriptions and metadata to enterprise data to improve discoverability 2. Demonstrate understanding of the Unity Catalog permission inheritance model
Topic 3: 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 4: Debugging and Deploying	- Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment - 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
Topic 5: 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 6: 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 7: Ensuring Data Security and Compliance	- 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 - Applying Data Security Mechanisms 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization 2. Use ACLs to secure workspace objects and enforce the principle of least privilege 3. Use row filters and column masks to protect sensitive table data
Topic 8: Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering 3. Design dimensional models for analytical workloads with efficient querying and aggregation 4. Design and implement scalable data models using Delta Lake to manage large datasets
Topic 9: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 2. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 3. Explain the advantages and disadvantages of streaming tables compared to materialized views 4. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 5. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 7. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 8. Create pipeline components using control flow operators such as if/else and foreach - Using Python and Tools for Development 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 3. Develop User-Defined Functions using Pandas/Python UDF
Topic 10: 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 Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use Query Profile and Spark UI to monitor workloads - 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 Sample Questions (Q72-Q77):

NEW QUESTION # 72
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: D

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

NEW QUESTION # 73
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. mergeSchema
B. checkpointLocation
C. partitionBy
D. overwriteSchema

Answer: B

Explanation:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates.
Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.

NEW QUESTION # 74
A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only

Answer: A

Explanation:
Using --start-time-from with today's midnight epoch ensures only runs that started during the current day are considered, and --completed-only filters out in-progress runs. This allows the script to reliably check whether the daily job has finished successfully before triggering downstream external actions.

NEW QUESTION # 75
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A. Federation creates local copies that must be manually refreshed.
B. Federation provides read-only access that reflects the current state of source systems.
C. A separate data synchronization service must be deployed.
D. Federation implements change data capture (CDC) from all sources.

Answer: B

Explanation:
Lakehouse Federation allows Databricks to query and manage external data sources through a single governance layer, without moving or copying data. The documentation specifies that
"Federated queries provide read-only access to data, reflecting the current state of the underlying source system." This ensures consistency across teams since all users access the same source of truth directly from the external system through Unity Catalog. Federation does not perform CDC replication or local caching; it queries live data on demand. Hence, option A accurately represents how Lakehouse Federation maintains consistency across federated sources.

NEW QUESTION # 76
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Answer: B

Explanation:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html

NEW QUESTION # 77
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