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Databricks Certified Professional Data Engineer exam is a certification program offered by Databricks, a unified data analytics platform that provides a collaborative workspace for data science teams. Databricks Certified Professional Data Engineer Exam certification program is designed for data professionals who want to demonstrate their knowledge and skills in building reliable, scalable, and performant data pipelines using Databricks.

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Databricks Certified Professional Data Engineer certification is globally recognized by various industries, including finance, healthcare, government, and technology. Databricks Certified Professional Data Engineer Exam certification validates the candidate's knowledge level in data engineering solutions and qualifies them to work with Databricks's technology. The certified professional can optimize and manage their organization's data in the cloud using Databricks, which results in timely and informed decisions.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is the output of below function when executed with input parameters 1, 3 :
1.def check_input(x,y):
2. if x < y:
3. x= x+1
4. if x>y:
5. x= x+1
6. if x <y:
7. x = x+1
8. return x

A. 0
B. 1
C. 2
D. 3
E. 4

Answer: E

NEW QUESTION # 12
Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

A. Setup an alert with custom Webhook destination
B. Use notebook and Jobs to use python to publish DQ metrics
C. Setup an alert with custom template
D. Setup an alert with dynamic template
E. Setup an alert to send an email, use python to parse email, and publish a webhook message

Answer: A

Explanation:
Explanation
Alerts supports multiple destinations, email is the default destination.
Alert destinations | Databricks on AWS
Graphical user interface, application Description automatically generated

NEW QUESTION # 13
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
C. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
D. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

Answer: C

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 14
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
C. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
D. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.

Answer: D

Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership: When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema does not automatically extend to ownership or permission of individual tables within that schema.
* Table Permissions: For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access: The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution: As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership (https://docs.databricks.com/security/access-control/workspace-acl.html#permissions

NEW QUESTION # 15
Which of the following is the correct statement for a session scoped temporary view?

A. Temporary views are lost once the notebook is detached and re-attached
B. Temporary views can be still accessed even if cluster is restarted
C. Temporary views can be still accessed even if the notebook is detached and attached
D. Temporary views are created in local_temp database
E. Temporary views stored in memory

Answer: A

Explanation:
Explanation
The answer is Temporary views are lost once the notebook is detached and attached There are two types of temporary views that can be created, Session scoped and Global
*A local/session scoped temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster, if a cluster restarts global temporary view is lost.

NEW QUESTION # 16
......

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