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

Section	Objectives
Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Schema evolution and enforcement - Time travel and versioning
Production Pipelines and Orchestration	- Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q58-Q63):

質問 # 58
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
D. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.

正解：C

解説：
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

質問 # 59
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
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C. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

正解：A

解説：
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

質問 # 60
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A. Nothing will occur until a COMMIT command is executed.
B. The table will be removed from the catalog but the data will remain in storage.
C. An error will occur because Delta Lake prevents the deletion of production data.
D. The table will be removed from the catalog and the data will be deleted.
E. Data will be marked as deleted but still recoverable with Time Travel.

正解：D

解説：
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.

質問 # 61
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A. Create a view without selecting the sensitive column.
B. Apply a column mask that references the group_access mapping table in its UDF.
C. Create a UDF that hardcodes allowed groups and apply it as a column mask.
D. Use a row filter to restrict access based on the user's group.

正解：B

解説：
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

質問 # 62
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D. A managed table will be created in the DBFS root storage container.
E. An external table will be created in the storage container mounted to /mnt/finance eda bucket.

正解：B

解説：
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language- SQL

質問 # 63
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