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

Section	Weight	Objectives
Topic 1: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions
Topic 2: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Topic 3: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Bronze, silver, gold data layers 3. Data governance fundamentals
Topic 4: Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems
Topic 5: 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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

A. USING CSV
TYPE ( "true","|")
B. FORMAT CSV
TYPE ( header ="true", delimiter = "|")
C. USING CSV
OPTIONS ( header ="true", delimiter = "|")
(Correct)
D. FORMAT CSV
FORMAT TYPE ( header ="true", delimiter = "|")
E. FORMAT CSV
OPTIONS ( "true","|")

Answer: C

Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"

NEW QUESTION # 183
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:
Comprehensive and Detailed
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 # 184
In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

A. Upgrade workspace to Unity catalog
B. Upgrade to DBR version 15.0
C. Migrate/upgrade objects in workspace managed/external tables/view to unity catalog
D. Copy data from workspace to unity catalog
E. Enable unity catalog feature in workspace settings

Answer: C

Explanation:
Explanation
Upgrade tables and views to Unity Catalog - Azure Databricks | Microsoft Docs Managed table: Upgrade a managed to Unity Catalog External table: Upgrade an external table to Unity Catalog

NEW QUESTION # 185
Create a schema called bronze using location '/mnt/delta/bronze', and check if the schema exists before creating.

A. Cannot create schema without a database
B. if IS_SCHEMA('bronze'): CREATE SCHEMA bronze LOCATION '/mnt/delta/bronze'
C. Schema creation is not available in metastore, it can only be done in Unity catalog UI
D. CREATE SCHEMA bronze IF NOT EXISTS LOCATION '/mnt/delta/bronze'
E. CREATE SCHEMA IF NOT EXISTS bronze LOCATION '/mnt/delta/bronze'

Answer: E

Explanation:
Explanation
https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-schema.html
1.CREATE SCHEMA [ IF NOT EXISTS ] schema_name [ LOCATION schema_directory ]

NEW QUESTION # 186
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can run
B. Can edit
C. Can manage
D. Can Read

Answer: D

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.References: Databricks documentation on access control and permissions for notebooks within the workspace (https://docs.databricks.com/security/access-control/workspace-acl.html).

NEW QUESTION # 187
......

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