Databricks-Certified-Data-Engineer-Associate Reliable Exam Prep, Examcollection Databricks-Certified-Data-Engineer-Associate Questions Answers by jihede6915 jihede6915

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To become certified as a Databricks data engineer associate, individuals must pass a rigorous certification exam that tests their knowledge and understanding of Databricks technologies and their ability to apply them in practical situations. The exam is designed to be challenging and requires individuals to have a deep understanding of data engineering concepts and technologies. However, individuals who successfully pass the exam can enjoy the benefits of becoming a certified Databricks data engineer associate and take their careers to the next level in the field of data engineering.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is a highly esteemed certification for data professionals. The exam measures an individual's competence in designing, building, and maintaining data pipelines using Databricks. The certification covers a wide range of topics, including data engineering fundamentals, Databricks architecture, data modeling, and data processing.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam designed to assess the skills and knowledge of data engineers who work with Databricks. Databricks is a cloud-based data platform that provides a unified analytics engine and collaborative workspace for data teams. The exam is intended for professionals who have experience with Databricks and want to demonstrate their expertise in data engineering.

GAQM Databricks Certified Data Engineer Associate Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would be able to collaborate on projects in real-timeB. Both teams would reorganize to report to the same departmentC. Both teams would use the same source of truth for their workD. Both teams would respond more quickly to ad-hoc requestsE. Both teams would autoscale their work as data size evolves

Answer: C

NEW QUESTION # 15
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.C. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.D. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.E. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.

Answer: D

NEW QUESTION # 16
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. UPDATE my_table WHERE age <= 25;B. UPDATE my_table WHERE age > 25;C. DELETE FROM my_table WHERE age > 25;D. SELECT * FROM my_table WHERE age > 25;E. DELETE FROM my_table WHERE age <= 25;

Answer: C

NEW QUESTION # 17
Which of the following describes the relationship between Bronze tables and raw data?

A. Bronze tables contain raw data with a schema applied.B. Bronze tables contain aggregates while raw data is unaggregated.C. Bronze tables contain less data than raw data files.D. Bronze tables contain more truthful data than raw data.E. Bronze tables contain a less refined view of data than raw data.

Answer: B

NEW QUESTION # 18
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day.
They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.C. They could only run the entire program on Sundays.D. They could submit a feature request with Databricks to add this functionality.E. They could redesign the data model to separate the data used in the final query into a new table.

Answer: A

NEW QUESTION # 19
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