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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question #1
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A. Use the "Validate" option in the notebook to check for syntax errors.
B. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
C. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
D. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.

Question #2
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
C. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
D. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

Question #3
Which of the following is true of Delta Lake and the Lakehouse?

A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B. Z-order can only be applied to numeric values stored in Delta Lake tables
C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

Question #4
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

Question #5
A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
B. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
C. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
D. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.

Solutions:

Question #1
Correct Answer: A

Question #2
Correct Answer: E

Question #3
Correct Answer: E

Question #4
Correct Answer: D

Question #5
Correct Answer: A