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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Passing Score:	70%
Available Languages:	Portuguese (Brazil), Japanese, Korean, English
Exam Price:	USD 200
Related Certifications:	Databricks Certified Associate Data Engineer
Exam Format:	Multiple Choice
Real Exam Qty:	59
Recommended Training:	Databricks Data Engineer Professional Training
Exam Registration:	Databricks Official Certification Registration
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored or onsite test center
Pre Condition:	No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-professional

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Databricks Certified Professional Data Engineer certification exam is an important credential for data engineers who work with Databricks. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidate's ability to design, build, and maintain data pipelines, as well as their knowledge of various data engineering tools and techniques. Databricks Certified Professional Data Engineer Exam certification can enhance the holder's career prospects and demonstrate their proficiency in using Databricks for data engineering tasks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
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 namedpredswith 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?

A.
B. preds.write.format("delta").save("/preds/churn_preds")
C.
D.
E. preds.write.mode("append").saveAsTable("churn_preds")

Answer: E

NEW QUESTION # 137
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?

A. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Answer: E

NEW QUESTION # 138
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?

A. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Answer: D

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake toensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
* transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes
* Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html

NEW QUESTION # 139
You are working on a marketing team request to identify customers with the same information between two tables CUSTOMERS_2021 and CUSTOMERS_2020 each table contains 25 columns with the same schema, You are looking to identify rows that match between two tables across all columns, which of the following can be used to perform in SQL

A. 1.SELECT * FROM CUSTOMERS_2021 C1
2.INNER JOIN CUSTOMERS_2020 C2
3.ON C1.CUSTOMER_ID = C2.CUSTOMER_ID
B. 1.SELECT * FROM CUSTOMERS_2021
2. UNION ALL
3.SELECT * FROM CUSTOMERS_2020
C. 1.SELECT * FROM CUSTOMERS_2021
2.EXCEPT
3.SELECT * FROM CUSTOMERS_2020
D. 1.SELECT * FROM CUSTOMERS_2021
2. UNION
3.SELECT * FROM CUSTOMERS_2020
E. 1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020

Answer: E

Explanation:
Explanation
Answer is,
1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020
To compare all the rows between both the tables across all the columns using intersect will help us achieve that, an inner join is only going to check if the same column value exists across both the tables on a single column.
INTERSECT [ALL | DISTINCT]
*Returns the set of rows which are in both subqueries.
If ALL is specified a row that appears multiple times in the subquery1 as well as in subquery will be returned multiple times.
If DISTINCT is specified the result does not contain duplicate rows. This is the default.

NEW QUESTION # 140
Which statement describes integration testing?

A. Requires manual intervention
B. Requires an automated testing framework
C. Validates behavior of individual elements of your application
D. Validates interactions between subsystems of your application
E. Validates an application use case

Answer: D

Explanation:
Explanation
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 141
......

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