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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:
Vendor:
Questions:
195
Last Updated:
Jan 26, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

The user_ltv table has the following schema:

email STRING, age INT, ltv INT

The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:

SELECT * FROM email_ltv

Which statement describes the results returned by this query?

Options:

A.

Three columns will be returned, but one column will be named "redacted" and contain only null values.

B.

Only the email and itv columns will be returned; the email column will contain all null values.

C.

The email and ltv columns will be returned with the values in user itv.

D.

The email, age. and ltv columns will be returned with the values in user ltv.

E.

Only the email and ltv columns will be returned; the email column will contain the string "REDACTED" in each row.

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Question 2

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?

Options:

A.

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.

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.

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.

D.

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.

E.

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.

Question 3

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.

Streaming DataFrame df has the following schema:

"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

Options:

A.

withWatermark("event_time", "10 minutes")

B.

awaitArrival("event_time", "10 minutes")

C.

await("event_time + ‘10 minutes'")

D.

slidingWindow("event_time", "10 minutes")

E.

delayWrite("event_time", "10 minutes")