[Q60-Q76] Databricks-Certified-Professional-Data-Engineer by Databricks Actual Free Exam Questions And Answers [UPDATED 2025]

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Databricks-Certified-Professional-Data-Engineer by Databricks Actual Free Exam Questions And Answers [UPDATED 2025]

Databricks-Certified-Professional-Data-Engineer Questions Truly Valid For Your Databricks Exam!

Databricks Certified Professional Data Engineer certification exam consists of 60 multiple-choice questions that need to be answered within 90 minutes. The questions are designed to test the candidate’s knowledge of Databricks and its features, as well as their ability to apply this knowledge to real-world scenarios. To pass the exam, candidates need to achieve a minimum score of 70%. Databricks Certified Professional Data Engineer Exam certification is valid for two years and can be renewed by retaking the exam or by completing a set of continuing education activities. Obtaining the Databricks Certified Professional Data Engineer certification can help data professionals demonstrate their expertise in building and deploying data engineering solutions using Databricks, which can be a valuable asset in today’s data-driven business landscape.

Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and hands-on exercises that test a candidate’s ability to design, build, and maintain data pipelines using the Databricks platform.

QUESTION 60
What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

1/3

2/3

1/6

2/6

QUESTION 61
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

No; Delta Lake manages streaming checkpoints in the transaction log.

Yes; both of the streams can share a single checkpoint directory.

No; only one stream can write to a Delta Lake table.

Yes; Delta Lake supports infinite concurrent writers.

No; each of the streams needs to have its own checkpoint directory.

QUESTION 62
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.

QUESTION 63
Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer’s claim?

Databricks Notebooks support commenting and notification comments

Databricks Notebooks support the creation of interactive data visualizations

Databricks Notebooks support real-time co-authoring on a single notebook

Databricks Notebooks support the use of multiple languages in the same notebook

Databricks Notebooks support automatic change-tracking and versioning

QUESTION 64
A junior data engineer seeks to leverage Delta Lake’s Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.

Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.

Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

QUESTION 65
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

QUESTION 66
A data team’s Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

Increase the shuffle partitions to account for additional aggregates

Specify a new checkpointlocation

Run REFRESH TABLE delta, /item_agg’

Remove .option (mergeSchema’, true’) from the streaming write

QUESTION 67
Which of the following is true, when building a Databricks SQL dashboard?

A dashboard can only use results from one query

Only one visualization can be developed with one query result

A dashboard can only connect to one schema/Database

More than one visualization can be developed using a single query result

A dashboard can only have one refresh schedule

QUESTION 68
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema “customer_id LONG, predictions DOUBLE, date DATE”.

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

preds.write.mode(“append”).saveAsTable(“churn_preds”)

preds.write.format(“delta”).save(“/preds/churn_preds”)

QUESTION 69
Which of the following results in the creation of an external table?

CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL

CREATE TABLE transactions (id int, desc string)

CREATE EXTERNAL TABLE transactions (id int, desc string)

CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL

CREATE TABLE transactions (id int, desc string) LOCATION ‘/mnt/delta/transactions’

QUESTION 70
A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for three consecutive days.
Ensure that notifications are not spammed during sustained issues.
Options:

SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP – INTERVAL ‘1’ DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours

WITH daily_avg AS (
SELECT DATE_TRUNC(‘DAY’, window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC(‘DAY’, window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once

SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP – INTERVAL ‘3’ DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated

SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP – INTERVAL ‘3’ DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once

QUESTION 71
Which of the statements are correct about lakehouse?

Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads

Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads

Lakehouse does not support ACID

In Lakehouse Storage and compute are coupled

Lakehouse supports schema enforcement and evolution

QUESTION 72
A data team’s Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

Increase the shuffle partitions to account for additional aggregates

Specify a new checkpointlocation

Run REFRESH TABLE delta, /item_agg’

Remove .option (mergeSchema’, true’) from the streaming write

QUESTION 73
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.

All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.

All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

QUESTION 74
Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql(“select count(*) from table”).collect()[0][0]

assert (row_count = 10, “Row count did not match”)

assert if (row_count = 10, “Row count did not match”)

assert row_count == 10, “Row count did not match”

assert if row_count == 10, “Row count did not match”

assert row_count = 10, “Row count did not match”

QUESTION 75
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing

All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated

All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped

QUESTION 76
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?

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.

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

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.

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

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.