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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Databricks Lakehouse Platform | 24% | - Lakehouse Architecture - Unity Catalog - Delta Lake - Data Management |
| Monitoring and Troubleshooting | 16% | - Performance Optimization - Monitoring - Troubleshooting |
| Data Modeling and Storage | 20% | - File Formats - Storage Optimization - Data Modeling |
| Data Quality and Governance | 12% | - Data Quality - Data Lineage - Governance |
| Data Processing | 28% | - ETL Pipelines - Structured Streaming - Data Transformation - Spark SQL |
Databricks Certified Data Engineer Professional Sample Questions:
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
- A. fs
- B. configure
- C. jobs
- D. libraries
- E. workspace
Correct Answer: A 🗳️
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The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:
For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:
Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?
- A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
- B. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
- C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
- D. Define a view against the products_per_order table and define the dashboard against this view.
- E. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
Correct Answer: E 🗳️
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?
- A. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
- B. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
- C. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
- D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
Correct Answer: D 🗳️
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A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?
- A. New data is written to a staging area and clustered during scheduled maintenance.
- B. New data is automatically clustered during write operations.
- C. New data is rejected if it doesn't match the clustering pattern.
- D. New data remains unclustered until the next OPTIMIZE operation.
Correct Answer: D 🗳️
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When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?
- A. The group needs to have USE CATALOG and USE SCHEMA on the catalog.
- B. The group should be made OWNER of the catalog.
- C. The group needs to have ALL PRIVILEGES on the catalog.
- D. The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
Correct Answer: A 🗳️
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