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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam designed for data engineers who are interested in validating their skills and knowledge in working with Databricks. Databricks is a cloud-based data processing and analytics platform that is widely used by organizations to manage and analyze large datasets. Databricks Certified Data Engineer Associate Exam certification exam is designed to test the candidate's ability to design, build and maintain data pipelines using Databricks.
NEW QUESTION # 315
Which of the following describes the relationship between Gold tables and Silver tables?
Answer: B
Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. Reference:
What is the medallion lakehouse architecture?
What is a Medallion Architecture?
NEW QUESTION # 316
A data engineer wants to track all changes made to a Delta Lake table over time, including inserts, updates, and deletes. The engineer needs to review previous versions of the table for auditing purposes. Which Delta Lake capability provides this functionality?
Answer: B
NEW QUESTION # 317
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?
Answer: E
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 # 318
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
Answer: D
Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.
NEW QUESTION # 319
A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?
Answer: B
NEW QUESTION # 320
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