QUIZ 2025 DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER: DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM UNPARALLELED LATEST PRACTICE MATERIALS

Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Unparalleled Latest Practice Materials

Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Unparalleled Latest Practice Materials

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Databricks Certified Professional Data Engineer certification exam is a hands-on exam that requires candidates to demonstrate their skills in building data pipelines and workflows using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of a set of performance-based tasks that require candidates to design, implement, and manage data solutions in a Databricks environment. Candidates are given a set of data engineering scenarios and must use Databricks to build solutions that meet the requirements of each scenario.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

  • A. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
  • B. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
  • C. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
  • D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
  • E. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

Answer: C

Explanation:
Explanation
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table


NEW QUESTION # 45
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

  • A. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
  • B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
  • C. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
  • D. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
  • E. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Answer: B

Explanation:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CREATE VIEW" section.


NEW QUESTION # 46
Which of the following locations in Databricks product architecture hosts jobs/pipelines and queries?

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

Answer: E

Explanation:
Explanation
The answer is Control Plane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Here is the product architecture diagram highlighted where


NEW QUESTION # 47
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

  • A. Run unit tests against non-production data that closely mirrors production
  • B. Define units test and functions within the same notebook
  • C. Define and import unit test functions from a separate Databricks notebook
  • D. Define and unit test functions using Files in Repos

Answer: A

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
References:
* Databricks Documentation on Testing: Testing and Validation of Data and Notebooks


NEW QUESTION # 48
Which of the statements is correct when choosing between lakehouse and Datawarehouse?

  • A. SQL support is only available for Traditional Datawarehouse's, Lakehouses support Python and Scala
  • B. Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.
  • C. Traditional Data warehouses have special indexes which are optimized for Machine learning
  • D. Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.
  • E. Traditional Data warehouses can serve low query latency with high reliability for BI workloads

Answer: D

Explanation:
Explanation
The lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that desire:
* Open, direct access to data stored in standard data formats.
* Indexing protocols optimized for machine learning and data science.
* Low query latency and high reliability for BI and advanced analytics.


NEW QUESTION # 49
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