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

Section	Weight	Objectives
Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement - Delta Lake fundamentals 1. ACID transactions 2. Time travel and data versioning 3. Optimize and Z-order
Data Ingestion	15-20%	- Batch ingestion methods 1. Spark APIs for ingestion 2. DBR autoloader 3. Integration with external systems - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses.
Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Store only the date value and calculate all time attributes dynamically in queries.
B. Store the date as a string in the format YYYY-MM-DD for readability.
C. Create separate dimension tables for different calendar systems (fiscal, academic, etc.).
D. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.

Answer: D

Explanation:
In dimensional modeling, Databricks recommends denormalized, attribute-rich dimension tables for performance and usability. A date dimension should include all commonly used derived time attributes such as fiscal period, quarter, month, weekday, and holiday flags. Precomputing these attributes ensures consistent business logic, eliminates repeated calculations during query time, and enables efficient filtering and aggregation. The documentation for Delta Lake and Lakehouse design explicitly advises precomputing these attributes for analytical workloads that depend heavily on time-based slicing. Options A and C degrade performance and consistency, while maintaining multiple calendar-specific dimension tables (B) complicates the model unnecessarily.

NEW QUESTION # 42
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: D

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time.
However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 43
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. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
B. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

Answer: C

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 # 44
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE DETAIL dev.pii test
B. SHOW TABLES dev
C. SHOW TBLPROPERTIES dev.pii test
D. DESCRIBE HISTORY dev.pii test
E. DESCRIBE EXTENDED dev.pii test

Answer: E

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 45
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for
15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.
Which code snippet correctly sets this retention period for deleted files?

A. spark.sql( " ALTER TABLE my_table SET TBLPROPERTIES ( ' delta.deletedFileRetentionDuration '
= ' interval 15 days ' ) " )
B. spark.conf.set( " spark.databricks.delta.deletedFileRetentionDuration " , " 15 days " )
C. spark.sql( " VACUUM my_table RETAIN 15 HOURS " )
D. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, " /mnt/data/my_table " )
deltaTable.deletedFileRetentionDuration = " interval 15 days "

Answer: A

Explanation:
In Delta Lake , the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
* By default, this retention duration is set to 7 days .
* To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ( ' delta.deletedFileRetentionDuration ' = ' interval 15 days ' ) This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
* B : This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
* C : VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration .
* D : Setting spark.conf applies a session-level configuration and does not permanently update the table' s retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.

NEW QUESTION # 46
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