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Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Professional-Data-Engineer
Exam Name:
Databricks Certified Data Engineer Professional Exam
Certification:
Databricks Certification
Vendor:
Databricks
Questions:
195
Last Updated:
Jan 1, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

Databricks-Certified-Professional-Data-Engineer: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Data Engineer Professional Exam Questions and Answers

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Question 1

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

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Question 2

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:

user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT

New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.

Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

Options:

A.

Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.

B.

Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

C.

Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

D.

Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.

E.

Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the

most recent value for each username.

Question 3

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.

The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.

Which statement exemplifies best practices for implementing this system?

Options:

A.

Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

B.

Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

C.

Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

D.

Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

E.

Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

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