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Test Databricks Databricks-Certified-Data-Engineer-Associate Dumps Pdf & Databricks-Certified-Data-Engineer-Associate Paper

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To pass the Databricks-Certified-Data-Engineer-Associate Exam, candidates must demonstrate their ability to work with the Databricks platform to design, build, and manage data pipelines. They must also be able to analyze data and provide insights to stakeholders using advanced analytics tools and techniques. This requires a deep understanding of data structures, algorithms, and programming languages like Python and SQL.

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Databricks-Certified-Data-Engineer-Associate Paper - Databricks-Certified-Data-Engineer-Associate Exam Details

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The Databricks Databricks-Certified-Data-Engineer-Associate Exam is conducted online and consists of 80 multiple-choice questions. The duration of the exam is 2 hours, and candidates must achieve a minimum score of 60% to pass. Databricks-Certified-Data-Engineer-Associate exam fee is $300, and candidates can schedule their exam at any time through the GAQM website. Databricks Certified Data Engineer Associate Exam certification is valid for three years and can be renewed by passing the latest version of the exam.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

A. An automated report needs to be runnable by all stakeholders.
B. An automated report needs to be made reproducible.
C. An automated report needs to be version-controlled across multiple collaborators.
D. An automated report needs to be tested to identify errors.
E. An automated report needs to be refreshed as quickly as possible.

Answer: A

NEW QUESTION # 75
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Databricks Filesystem
B. Dashboards
C. Repos
D. Data Explorer
E. Jobs

Answer: D

Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. Reference:
Data Explorer
Access control for Delta tables
SHOW GRANT
[GRANT]

NEW QUESTION # 76
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 was external
B. The table was managed
C. The table's data was smaller than 10 GB
D. The table did not have a location
E. The table's data was larger than 10 GB

Answer: B

Explanation:
The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.
Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table. Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.
Option C is not correct, for the same reason as option B.
Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.
Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.
Reference:
Managing Tables
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 77
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace predict with a stream-friendly prediction function
B. Replace format("delta") with format("stream")
C. Replace "transactions" with the path to the location of the Delta table
D. Replace spark.read with spark.readStream
E. Replace schema(schema) with option ("maxFilesPerTrigger", 1)

Answer: D

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 78
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 redesign the data model to separate the data used in the final query into a new table.
B. They could only run the entire program on Sundays.
C. They could submit a feature request with Databricks to add this functionality.
D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
E. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.

Answer: E

NEW QUESTION # 79
......

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