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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The number of times the job definition has been run in the workspace.
B. The job_id and number of times the job has been are concatenated and returned.
C. The job_id is returned in this field.
D. The globally unique ID of the newly triggered run.
E. The total number of jobs that have been run in the workspace.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #2

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
B. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
C. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
D. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Question #3

A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A. Repair the run with the new parameter.
B. Update the task by adding the missing task parameter, and manually run the job.
C. Share the analysis with the failing notebook owner so that they can fix it quickly.
D. Repair the run with the new parameter, and update the task by adding the missing task parameter.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #4

A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
B. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
C. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
D. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #5

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. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
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 at query time and return the result of joining the valid versions of the source tables at the time the query began.
D. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.

Reveal Solution Discussion 0

Correct Answer: C 🗳️