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Databricks Certified-Data-Engineer-Professional Actual Tests : Databricks Certified Data Engineer Professional

Certified-Data-Engineer-Professional actual test
  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Oct 21, 2026
  • Q & A: 250 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About Databricks Certified-Data-Engineer-Professional Exam Actual Tests

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

SectionObjectives
Topic 1: 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 Query Profiler and Spark UI to monitor workloads
        • 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
          • 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
            • 4. Use system tables for resource, cost, audit, and workload monitoring
              Topic 2: Data Transformation, Cleansing, and Quality- Data Quality
              • 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                • 2. Develop data quarantining processes for invalid data
                  - Advanced Data Transformation
                  • 1. Apply window functions, joins, and aggregations to large datasets
                    • 2. Write efficient Spark SQL and PySpark transformations
                      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 UDFs
                        • 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                          • 3. Manage and troubleshoot third-party library installations and dependencies
                            - Building and Testing ETL Pipelines
                            • 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                              • 2. Compare streaming tables and materialized views
                                • 3. Configure environments, dependencies, memory, and retry behavior
                                  • 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                    • 5. Use APPLY CHANGES APIs for change data capture
                                      • 6. Develop unit and integration tests for data processing code
                                        • 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                          • 8. Use control flow operators in pipeline components
                                            Topic 4: Ensuring Data Security and Compliance- Data Security
                                            • 1. Apply anonymization and pseudonymization techniques
                                              • 2. Use row filters and column masks for sensitive data
                                                • 3. Use ACLs to secure workspace objects and enforce least privilege
                                                  - Compliance
                                                  • 1. Implement pipelines that detect and mask personally identifiable information
                                                    • 2. Develop data purging solutions according to data retention policies
                                                      Topic 5: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                                      • 1. Build append-only pipelines for batch and streaming data using Delta
                                                        • 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                                          • 3. Ingest data from message buses and cloud storage
                                                            Topic 6: Debugging and Deploying- 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
                                                                  - Deploying CI/CD
                                                                  • 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                                    • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                                                      Topic 7: Data Governance- Unity Catalog Permissions
                                                                      • 1. Understand the Unity Catalog permission inheritance model
                                                                        - Metadata and Discoverability
                                                                        • 1. Create and maintain descriptions and metadata for enterprise data
                                                                          Topic 8: Cost & Performance Optimisation- Cost Optimization
                                                                          • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                                            - Delta Optimization
                                                                            • 1. Apply data skipping and file pruning techniques
                                                                              • 2. Use Change Data Feed to address streaming table limitations and improve latency
                                                                                • 3. Understand deletion vectors and liquid clustering
                                                                                  - Query Performance
                                                                                  • 1. Use Query Profile to identify performance bottlenecks
                                                                                    • 2. Identify inefficient joins and excessive data shuffling
                                                                                      Topic 9: 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 10: Data Modelling- Scalable Data Models
                                                                                              • 1. Design and implement scalable data models using Delta Lake
                                                                                                • 2. Optimize data layout using Liquid Clustering
                                                                                                  • 3. Understand Liquid Clustering versus partitioning and Z-Ordering
                                                                                                    - Dimensional Modelling
                                                                                                    • 1. Design dimensional models for analytical workloads

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question #1

                                                                                                      The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
                                                                                                      Which approach will ensure that this requirement is met?

                                                                                                      • A. Whenever a database is being created, make sure that the location keyword is used
                                                                                                      • B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
                                                                                                      • C. When tables are created, make sure that the external keyword is used in the create table statement.
                                                                                                      • D. Whenever a table is being created, make sure that the location keyword is used.
                                                                                                      • E. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: D  🗳️

                                                                                                      Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).

                                                                                                      Question #2

                                                                                                      A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
                                                                                                      Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

                                                                                                      • A. databricks run my_project_job -t prod
                                                                                                      • B. databricks execute my_project_job -e prod
                                                                                                      • C. databricks bundle run my_project_job -t prod
                                                                                                      • D. databricks job run my_project_job --env prod
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

                                                                                                      Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).

                                                                                                      Question #3

                                                                                                      Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
                                                                                                      Which statement describes a limitation of Databricks Secrets?

                                                                                                      • A. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
                                                                                                      • B. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
                                                                                                      • C. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
                                                                                                      • D. Iterating through a stored secret and printing each character will display secret contents in plain text.
                                                                                                      • E. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).

                                                                                                      Question #4

                                                                                                      The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

                                                                                                      The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
                                                                                                      Which code block accomplishes this task while minimizing potential compute costs?

                                                                                                      • A.
                                                                                                      • B.
                                                                                                      • C. preds.write.mode("append").saveAsTable("churn_preds")
                                                                                                      • D.
                                                                                                      • E. preds.write.format("delta").save("/preds/churn_preds")
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

                                                                                                      Question #5

                                                                                                      The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

                                                                                                      • A. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
                                                                                                      • B. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
                                                                                                      • C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
                                                                                                      • D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).

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