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

Certified-Data-Engineer-Professional Exam Simulator
  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Oct 14, 2026
  • Q & A: 250 Questions and Answers

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

SectionObjectives
Topic 1: 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
      Topic 2: Ensuring Data Security and Compliance- Ensuring Compliance
      • 1. Develop data purging solutions that comply with data retention policies
        • 2. Implement compliant batch and streaming pipelines that detect and mask PII
          - Applying Data Security Mechanisms
          • 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
            • 2. Use row filters and column masks to protect sensitive table data
              • 3. Use ACLs to secure workspace objects and enforce the principle of least privilege
                Topic 3: 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. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                      • 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                        • 5. Apply Change Data Feed to address streaming table limitations and improve latency
                          Topic 4: 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. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                              • 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                - 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 5: Monitoring and Alerting- Monitoring
                                    • 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                      • 2. Use Query Profile and Spark UI to monitor workloads
                                        • 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                          • 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                            - 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 6: 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 7: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                                        • 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                                                          • 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                                                            Topic 8: Data Governance- Govern enterprise data
                                                            • 1. Demonstrate understanding of the Unity Catalog permission inheritance model
                                                              • 2. Create and add descriptions and metadata to enterprise data to improve discoverability
                                                                Topic 9: Developing Code for Data Processing using Python and SQL- 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. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                                    • 3. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                                      • 4. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                        • 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                                          • 6. Create pipeline components using control flow operators such as if/else and foreach
                                                                            • 7. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                                              • 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                                - Using Python and Tools for Development
                                                                                • 1. Develop User-Defined Functions using Pandas/Python UDF
                                                                                  • 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                                    • 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                                                      Topic 10: Data Sharing and Federation- Share and federate data
                                                                                      • 1. Configure Lakehouse Federation with appropriate governance across supported source systems
                                                                                        • 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                                                                          • 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            Question #1

                                                                                            A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

                                                                                            • A. Traditional batch processing with scheduled Databricks Jobs
                                                                                            • B. Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
                                                                                            • C. Auto Loader with schema inference and evolution enabled
                                                                                            • D. COPY INTO SQL command with parameters to track processed files
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: C  🗳️

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                                                                                            Question #2

                                                                                            A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

                                                                                            • A. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
                                                                                            • B. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
                                                                                            • C. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
                                                                                            • D. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: C  🗳️

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

                                                                                            Question #3

                                                                                            A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
                                                                                            A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

                                                                                            • A. Delete the failed run, disable the cluster policy, and re-execute all tasks.
                                                                                            • B. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
                                                                                            • C. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
                                                                                            • D. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: D  🗳️

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                                                                                            Question #4

                                                                                            What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

                                                                                            • A. PII masking is only required for direct identifiers.
                                                                                            • B. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
                                                                                            • C. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
                                                                                            • D. Dynamic data masking is applied only at rest, so it does not affect query performance.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: C  🗳️

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                                                                                            Question #5

                                                                                            A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
                                                                                            The user_ltv table has the following schema:
                                                                                            email STRING, age INT, ltv INT
                                                                                            The following view definition is executed:

                                                                                            An analyst who is not a member of the marketing group executes the following query:
                                                                                            SELECT * FROM email_ltv
                                                                                            Which statement describes the results returned by this query?

                                                                                            • A. The email and ltv columns will be returned with the values in user itv.
                                                                                            • B. The email, age. and ltv columns will be returned with the values in user ltv.
                                                                                            • C. Only the email and ltv columns will be returned; the email column will contain the string
                                                                                              "REDACTED" in each row.
                                                                                            • D. Three columns will be returned, but one column will be named "redacted" and contain only null values.
                                                                                            • E. Only the email and itv columns will be returned; the email column will contain all null values.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: C  🗳️

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

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