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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Related Certifications: | Databricks Certified Data Analyst Associate Databricks Certified Associate Developer |
| Available Languages: | English |
| Passing Score: | 70% |
| Real Exam Qty: | 60 |
| Exam Format: | Multiple Select, Multiple Choice |
| Sample Questions: | Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) |
| Pre Condition: | Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/professional-data-engineer |
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The Databricks Certified Professional Data Engineer Exam certification exam is a computer-based test that consists of multiple-choice questions. Candidates have two hours to complete the exam, and they must achieve a minimum score of 70% to pass. Databricks-Certified-Professional-Data-Engineer exam is proctored, and candidates must have a reliable internet connection and a computer with a webcam and microphone to take the test.
Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Exam is a certification exam designed to test the knowledge and skills of data engineers who use Databricks to build and manage data pipelines. Databricks is a cloud-based data processing and analytics platform that provides a unified workspace for data scientists, data engineers, and business analysts to collaborate and work with large-scale data. Databricks-Certified-Professional-Data-Engineer Exam is intended for data engineers who have experience in developing and maintaining data pipelines using Databricks and are looking to validate their skills and knowledge.
NEW QUESTION # 97
A Delta Lake table was created with the below query:
Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?
Answer: B
Explanation:
Explanation
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table
NEW QUESTION # 98
Which of the following SQL commands are used to append rows to an existing delta table?
Answer: E
Explanation:
Explanation
The answer is INSERT INTO table_name
Insert adds rows to an existing table, this is very similar to add rows a traditional Database or Da-tawarehouse.
NEW QUESTION # 99
Which of the following is correct for the global temporary view?
Answer: B
Explanation:
Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.
NEW QUESTION # 100
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer
"Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Answer: C
Explanation:
The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References:
* Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html
* Job permissions:
https://docs.databricks.com/security/access-control/table-acls/privileges.html#job-permissions
NEW QUESTION # 101
Which statement characterizes the general programming model used by Spark Structured Streaming?
Answer: B
Explanation:
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.
NEW QUESTION # 102
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