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Databricks-Certified-Data-Engineer-Associate exam is a comprehensive exam that tests the individual's knowledge of Databricks and its various features. Databricks-Certified-Data-Engineer-Associate exam includes multiple-choice questions that require the individual to select the best answer from a list of options. Databricks-Certified-Data-Engineer-Associate exam also includes hands-on tasks that require the individual to demonstrate their ability to perform specific tasks using Databricks.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to fail before the new task begins
B. When another task needs to be replaced by the new task
C. When another task needs to successfully complete before the new task begins
D. When another task needs to use as little compute resources as possible
E. When another task has the same dependency libraries as the new task

Answer: C

NEW QUESTION # 99
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. autoloader
B. DELTA
C. org.apache.spark.sql.jdbc
D. org.apache.spark.sql.sqlite
E. sqlite

Answer: C

Explanation:
Explanation
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql

NEW QUESTION # 100
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. dbfs:/user/hive/customer360
B. More information is needed to determine the correct response
C. dbfs:/user/hive/database/customer360
D. dbfs:/user/hive/warehouse

Answer: D

Explanation:
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
Reference:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 101
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's data was smaller than 10 GB
B. The table's data was larger than 10 GB
C. The table was managed
D. The table was external
E. The table did not have a location

Answer: D

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 # 102
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A. Avoiding vendor lock-in
B. Ability to scale storage
C. Cloud-specific integrations
D. Simplified governance
E. Ability to scale workloads

Answer: A

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

NEW QUESTION # 103
......

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