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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification program designed to assess an individual's expertise in data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the practical knowledge and skills required to work with big data using Databricks, a cloud-based data analytics platform.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q71-Q76):
NEW QUESTION # 71
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?
- A. The table's data was larger than 10 GB
- B. The table was managed
- C. The table's data was smaller than 10 GB
- D. The table was external
- E. The table did not have a location
Answer: D
Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location.hence when a external table is dropped you clear off only the metadata and the files (data) remain.
NEW QUESTION # 72
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 reduce the cluster size of the SQL endpoint.
- B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- C. They can turn on the Auto Stop feature for the SQL endpoint.
- D. They can set up the dashboard's SQL endpoint to be serverless.
- E. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
Answer: D
Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API
NEW QUESTION # 73
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The DELETE HISTORY command was run on the table
- B. The VACUUM command was run on the table
- C. The HISTORY command was run on the table
- D. The TIME TRAVEL command was run on the table
- E. The OPTIMIZE command was nun on the table
Answer: B
Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]
NEW QUESTION # 74
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. org.apache.spark.sql.sqlite
- B. sqlite
- C. DELTA
- D. autoloader
- E. org.apache.spark.sql.jdbc
Answer: E
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 # 75
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
- A.
- B.
- C.
- D.
- E.
Answer: D
Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp
NEW QUESTION # 76
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