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

Section	Weight	Objectives
Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code
Data Transformation, Cleansing, and Quality	10%	- Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management
Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture
Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads
Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit
Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance
Data Governance	7%	- Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards
Data Sharing and Federation	5%	- Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing
Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines
Monitoring and Alerting	10%	- Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q51-Q56):

51. Frage
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
B. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
C. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
D. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
E. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

Antwort: E

52. Frage
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
B. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
C. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
E. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

Antwort: E

Begründung:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

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

Antwort: A

Begründung:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

54. Frage
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
B. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
C. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
E. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

Antwort: A

Begründung:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.

55. Frage
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Periodically run OPTIMIZE table_name.
B. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
C. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
D. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
E. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

Antwort: A,C

Begründung:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.

56. Frage
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