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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Periodically run OPTIMIZE table_name.
B. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
C. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
D. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Question 2

When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Question 3

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. At some point, the upstream data provider switched everything to multi-line JSON.
B. The badRecordsPath location is accumulating many small files.
C. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
D. The source data is valid JSON but does not conform to the defined schema in some way.

Question 4

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
B. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
C. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
D. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

Question 5

A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

A.

B.

C.

D.