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To take the Databricks Certified Professional Data Engineer certification exam, candidates must have a solid understanding of data engineering concepts, as well as experience using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks, which require candidates to demonstrate their ability to perform specific data engineering tasks using Databricks.
Databricks Certified Professional Data Engineer is a certification exam that tests the skills and knowledge required to design and implement data solutions using Databricks. Databricks is a cloud-based data platform that helps organizations manage and process large amounts of data. Databricks Certified Professional Data Engineer Exam certification exam is designed for data engineers who are responsible for creating and maintaining data pipelines, managing data storage, and implementing data solutions.
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The aim of the Databricks-Certified-Professional-Data-Engineer Certification is to create a standard for data engineering skills in the big data industry. Databricks Certified Professional Data Engineer Exam certification demonstrates that professionals have the knowledge and skills needed to work effectively on complex big data projects in the cloud. It also improves the candidate’s chances of getting hired, retaining their job, or earning a promotion in a highly competitive industry.
NEW QUESTION # 23
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
Answer: B
NEW QUESTION # 24
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: B
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 # 25
A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)
Answer: D,E
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks recommends addressing disk spilling-which occurs when Spark tasks run out of memory-by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory-to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory. Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D.
NEW QUESTION # 26
A data architect is designing a Databricks solution to efficiently process data for different business requirements.
In which scenario should a data engineer use a materialized view compared to a streaming table ?
Answer: C
Explanation:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing , enabling event- driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C , where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.
NEW QUESTION # 27
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
Answer: D
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 # 28
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