Free PDF Quiz 2026 Newest Databricks Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Mock Exams

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Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their skills and proficiency in using Databricks for data engineering tasks. Databricks Certified Professional Data Engineer Exam certification can help data engineers to advance their careers and increase their earning potential. It can also help organizations to identify and hire skilled data engineers who can design and implement data solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which of the following is true of Delta Lake and the Lakehouse?

A. Z-order can only be applied to numeric values stored in Delta Lake tables
B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

Answer: B

Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
* Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
* Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3.
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
* Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
* Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]

NEW QUESTION # 16
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

A. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
B. Appends the data to the current version, you can time travel to previous versions
C. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.
D. By default, overwrites the data and schema, you cannot perform time travel
E. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

Answer: E

Explanation:
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.

NEW QUESTION # 17
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
B. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
C. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
D. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Answer: D

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 18
Where are Interactive notebook results stored in Databricks product architecture?

A. Control plane
B. Data plane
C. Databricks web application
D. JDBC data source
E. Data and Control plane

Answer: E

Explanation:
Explanation
The answer is Data and Control plane,
Only Job results are stored in Data Plane(your storage), Interactive notebook results are stored in a combination of the control plane (partial results for presentation in the UI) and customer storage.
https://docs.microsoft.com/en-us/azure/databricks/getting-started/overview#--high-level-architecture Snippet from the above documentation, Graphical user interface, application Description automatically generated

How to change this behavior?
You can change this behavior using Workspace/Admin Console settings for that workspace, once enabled all of the interactive results are stored in the customer account(data plane) except the new notebook visualization feature Databricks has recently introduced, this still stores some metadata in the control pane irrespective of the below settings. please refer to the documentation for more details.
Graphical user interface, text, application, email Description automatically generated

Why is this important to know?
I recently worked on a project where we had to deal with sensitive information of customers and we had a security requirement that all of the data need to be stored in the data plane including notebook results.

NEW QUESTION # 19
Create a schema called bronze using location '/mnt/delta/bronze', and check if the schema exists before creating.

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

Answer: C

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 # 20
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