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Databricks Certified Professional Data Engineer certification is an excellent choice for individuals who are looking to specialize in data engineering and want to demonstrate their expertise in Databricks technologies. It is also a valuable credential for companies that use Databricks and want to ensure that their employees have the necessary skills to manage and analyze large amounts of data effectively.

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To take the Databricks Certified Professional Data Engineer certification exam, candidates must have a solid understanding of data engineering concepts, as well as experience using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks, which require candidates to demonstrate their ability to perform specific data engineering tasks using Databricks.

To prepare for the DCPDE exam, candidates should have a solid understanding of data engineering concepts, such as data modeling, data integration, data transformation, and data quality. They should also have experience working with big data technologies, such as Apache Spark, Apache Kafka, and Apache Hadoop.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
Once a cluster is deleted, below additional actions need to performed by the administrator

A. No action needs to be performed. All resources are automatically removed.
B. Remove networking but Virtual machines and storage disks are automatically dropped
C. Remove virtual machines but storage and networking are automatically dropped
D. Remove logs
E. Drop storage disks but Virtual machines and networking are automatically dropped

Answer: A

Explanation:
Explanation
What is Delta?
Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse

NEW QUESTION # 96
The team has decided to take advantage of table properties to identify a business owner for each table, which of the following table DDL syntax allows you to populate a table property identifying the business owner of a table CREATE TABLE inventory (id INT, units FLOAT)

A. CREATE TABLE inventory (id INT, units FLOAT)
SET PROPERTY (business_owner = 'supply chain')
B. CREATE TABLE inventory (id INT, units FLOAT)
SET TAG (business_owner = 'supply chain')
C. SET TBLPROPERTIES business_owner = 'supply chain'
CREATE TABLE inventory (id INT, units FLOAT)
D. CREATE TABLE inventory (id INT, units FLOAT)
SET (business_owner = 'supply chain')
E. TBLPROPERTIES (business_owner = 'supply chain')

Answer: E

Explanation:
Explanation
CREATE TABLE inventory (id INT, units FLOAT) TBLPROPERTIES (business_owner = 'supply chain') Table properties and table options (Databricks SQL) | Databricks on AWS Alter table command can used to update the TBLPROPERTIES ALTER TABLE inventory SET TBLPROPERTIES(business_owner , 'operations')

NEW QUESTION # 97
The data engineering team maintains the following code:

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

A. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
B. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
C. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
D. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
E. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

Answer: D

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 98
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
C. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

Answer: B

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.
Reference: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 99
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. When a database is being created, make sure that the LOCATION keyword is used.
C. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
E. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

Answer: C

Explanation:
Explanation
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
References:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table

NEW QUESTION # 100
......

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