Databricks Certified Data Engineer Professional : Certified-Data-Engineer-Professional

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Exam Code: Certified-Data-Engineer-Professional

Exam Name: Databricks Certified Data Engineer Professional

Updated: Oct 06, 2026

Q & A: 250 Questions and Answers

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

Section	Objectives
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Explain the advantages and disadvantages of streaming tables compared to materialized views 2. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 3. Create pipeline components using control flow operators such as if/else and foreach 4. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 5. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 6. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 7. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 8. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior - Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Develop User-Defined Functions using Pandas/Python UDF 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
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. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
Monitoring and Alerting	- 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 - Monitoring 1. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 2. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 3. Use Query Profile and Spark UI to monitor workloads 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Simplify data layout decisions and optimize query performance using liquid clustering 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
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
Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Apply Change Data Feed to address streaming table limitations and improve latency 4. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 5. Understand Delta optimization techniques such as deletion vectors and liquid clustering
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
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
Ensuring Data Security and Compliance	- 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 - 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
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

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

A. Workspace users receive USE CATALOG and specific privileges on default schema only.
B. Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically
C. Workspace catalog permissions are not subject to inheritance rules.
D. New users only receive CREATE TABLE privileges on the default schema.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #2

A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
B. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
C. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
D. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

Question #3

A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A. Enable automatic file compaction on writes
B. Enable change data feed on the Delta table
C. Partition the Delta table by account_id
D. Enable deletion vectors on the Delta table

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #4

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

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #5

A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
B. Store the date as string in ISO format (YYYY-MM-DD) for readability.
C. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
D. Store only the date value and calculate all time attributes in queries.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Explanation: Only visible for PDFDumps members. You can sign-up / login (it's free).

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Databricks Certified Data Engineer Professional : Certified-Data-Engineer-Professional

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