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

Section	Weight	Objectives
Data Processing	28%	- Data Transformation - Structured Streaming - Spark SQL - ETL Pipelines
Databricks Lakehouse Platform	24%	- Data Management - Unity Catalog - Lakehouse Architecture - Delta Lake
Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
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. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
D. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Answer: A,D

Explanation:
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.

NEW QUESTION # 155
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. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

Answer: A

Explanation:
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.

NEW QUESTION # 156
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
B. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
C. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

Answer: D

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

NEW QUESTION # 157
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
B. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
C. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
D. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
E. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

Answer: E

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed.

NEW QUESTION # 158
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 ALL PRIVILEGES and the MANAGE on the catalog.
B. The group needs to have ALL PRIVILEGES on the catalog.
C. The group needs to have USE CATALOG and USE SCHEMA on the catalog.
D. The group should be made OWNER of the catalog.

Answer: C

Explanation:
Granting the project group USE CATALOG (and USE SCHEMA on existing schemas) allows team members to work within the catalog without giving them ownership or administrative control.
This enables teams to create and manage schemas, tables, and volumes inside the catalog while preventing them from renaming, deleting, or modifying catalog-level permissions, which remain under central IT governance.

NEW QUESTION # 159
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