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

Section	Objectives
Topic 1: 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 SQL Alerts to monitor data quality 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
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: Developing Code for Data Processing using Python and SQL	- 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 - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 2. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 3. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 4. Explain the advantages and disadvantages of streaming tables compared to materialized views 5. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 6. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 7. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 8. Create pipeline components using control flow operators such as if/else and foreach
Topic 4: Cost & Performance Optimization	- Optimize cost and performance 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 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. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 5. Apply Change Data Feed to address streaming table limitations and improve latency
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: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors - 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
Topic 7: 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 8: 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 9: Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Design dimensional models for analytical workloads with efficient querying and aggregation 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Simplify data layout decisions and optimize query performance using liquid clustering
Topic 10: 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

Everything Candidates Want to Know About the Certified-Data-Engineer-Professional Exam

What exactly is the Certified-Data-Engineer-Professional exam?

The Databricks Certified Data Engineer Professional exam is the official Databricks assessment behind the Databricks Certified Data Engineer Professional credential, which sits at the Professional level. Passing it confirms that your skills meet the vendor's current requirements rather than a textbook outline. It also connects with related certifications such as Databricks Certified Data Engineer Associate, so it can anchor a broader certification path.

How many questions are on the Certified-Data-Engineer-Professional exam, and how long does it take?

According to the official exam information, the Databricks Certified Data Engineer Professional exam includes 59 scored multiple-choice questions questions and gives you 120 minutes to complete them. Treat that as a pacing exercise, not just a knowledge check: bank the questions you know first, flag the ones that stall you, and circle back instead of burning minutes on a single item. Before test day, run at least one full timed session in the GuideTorrent desktop or online test engine, so the clock never feels unfamiliar when it counts.

Are there any prerequisites for taking the Certified-Data-Engineer-Professional exam?

The official prerequisites for the Databricks Certified Data Engineer Professional exam are as follows: No prerequisite is required. Related course attendance and one year of hands-on experience in data engineering tasks covered by the exam are highly recommended.. Requirements can change over time, so confirm the details on the official Databricks exam page at https://www.databricks.com/sites/default/files/2025-11/databricks-certified-data-engineer-professional-exam-guide-november-30-2025.pdf before you book your seat.

How do I register for the Certified-Data-Engineer-Professional exam?

You can register through the official channels listed below:

Databricks Certified Data Engineer Professional Certification

Exam delivery: Online proctored or test center proctored.

What training does Databricks recommend for the Certified-Data-Engineer-Professional exam?

Databricks points candidates toward the following official training options:

Official training builds the theory; the 250 practice questions from GuideTorrent show you how that theory appears in exam-style items, which is where most study plans actually pay off.

Can I try the Certified-Data-Engineer-Professional practice questions before buying?

Yes. A free PDF demo of the Databricks Certified Data Engineer Professional practice questions is available on the GuideTorrent samples page, so you can check the question style and difficulty before spending anything. Every purchase also includes 365 days of free updates, and if your product expires after that period, you can extend the update service from your member zone at 50% off.

What if I fail the Certified-Data-Engineer-Professional exam, and how is the product delivered?

If you take the Certified-Data-Engineer-Professional exam within 60 days of your purchase and do not pass, GuideTorrent offers a 100% money-back guarantee: send a scanned copy of your exam enrollment slip together with the official Score Report PDF within two days of your exam date, and the refund is processed within seven days. The candidate name must match the payer name, and the guarantee does not apply to exams taken within three days of purchase, to material that was downloaded without the exam actually being taken, or to free materials and expired orders. Prefer to keep studying instead? You can exchange your purchase for two additional exam products of equal value, free of charge, while keeping the update service on your original product. Delivery itself is instant: the download link is emailed within one minute of payment, and if nothing arrives within two hours, our support team will sort it out. There is no limit on how many computers you install the material on.

What topics are covered in the Certified-Data-Engineer-Professional exam?

The Databricks Certified Data Engineer Professional syllabus is organized into 10 domains. The leading areas include Data Sharing and Federation, Developing Code for Data Processing using Python and SQL, and Data Transformation, Cleansing, and Quality. For the complete, topic-by-topic breakdown, scroll up to the Exam Topics section above.

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #2

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
B. "Can Manage" privileges on the required cluster
C. Cluster creation allowed. "Can Restart" privileges on the required cluster
D. "Can Restart" privileges on the required cluster
E. Cluster creation allowed. "Can Attach To" privileges on the required cluster

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #3

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. Integration
B. Manual
C. End-to-end
D. functional
E. Unit

Reveal Solution Discussion 0

Correct Answer: E 🗳️

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Question #4

A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
D. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #5

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

Reveal Solution Discussion 0

Correct Answer: B 🗳️