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2025 100% Free Databricks-Certified-Data-Engineer-Associate–High Pass-Rate 100% Free Online Exam | New Databricks-Certified-Data-Engineer-Associate Practice Materials

2025 100% Free Databricks-Certified-Data-Engineer-Associate–High Pass-Rate 100% Free Online Exam | New Databricks-Certified-Data-Engineer-Associate Practice Materials

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Databricks Certified Data Engineer Associate certification is an essential certification for individuals who want to pursue a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics and is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines. By earning this certification, candidates can demonstrate their expertise in data engineering and increase their chances of getting hired by top companies in the data and AI industry.

Databricks Certified Data Engineer Associate certification exam covers topics such as data engineering concepts, data ingestion, data processing, data storage, and data transformation using Apache Spark and Delta Lake. Candidates who Pass Databricks-Certified-Data-Engineer-Associate Exam will have a deep understanding of the Databricks platform and will be able to design, build, and maintain data pipelines that are scalable, reliable, and efficient. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data analysts, and data scientists who work with big data and want to enhance their skills and advance their careers.

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To take the Databricks-Certified-Data-Engineer-Associate Exam, individuals must have a good understanding of data engineering concepts and experience working with Databricks. Databricks-Certified-Data-Engineer-Associate exam consists of multiple-choice questions that cover a wide range of topics, including data ingestion, data transformation, data storage, and data analysis. Candidates must score a passing grade to earn the certification.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

  • A. They can turn on the Auto Stop feature for the SQL endpoint.
  • B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
  • C. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
  • D. They can reduce the cluster size of the SQL endpoint.
  • E. They can set up the dashboard's SQL endpoint to be serverless.

Answer: A


NEW QUESTION # 37
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

  • A. The table was external
  • B. The table's data was larger than 10 GB
  • C. The table's data was smaller than 10 GB
  • D. The table was managed
  • E. The table did not have a location

Answer: A

Explanation:
An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. Reference: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table


NEW QUESTION # 38
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

  • A. Databricks Filesystem
  • B. Driver node
  • C. Worker node
  • D. Databricks web application
  • E. JDBC data source

Answer: D

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. Reference: Databricks architecture overview, Security and Trust Center


NEW QUESTION # 39
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which command can be used to grant full permissions on the database to the new data engineering team?

  • A. grant all privileges on table sales TO team;
  • B. GRANT ALL PRIVILEGES ON TABLE team TO sales;
  • C. GRANT SELECT ON TABLE sales TO team;
  • D. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;

Answer: A

Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
Reference:
Databricks documentation on SQL permissions: SQL Permissions in Databricks


NEW QUESTION # 40
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

  • A. The table was external
  • B. The table's data was larger than 10 GB
  • C. The table's data was smaller than 10 GB
  • D. The table was managed
  • E. The table did not have a location

Answer: A

Explanation:
Explanation
The reason why the data files still exist while the metadata files were deleted is because the table was external.
When a table is external in Spark SQL (or in other database systems), it means that the table metadata (such as schema information and table structure) is managed externally, and Spark SQL assumes that the data is managed and maintained outside of the system. Therefore, when you execute a DROP TABLE statement for an external table, it removes only the table metadata from the catalog, leaving the data files intact. On the other hand, for managed tables (option E), Spark SQL manages both the metadata and the data files. When you drop a managed table, it deletes both the metadata and the associated data files, resulting in a complete removal of the table.


NEW QUESTION # 41
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