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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.
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.
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NEW QUESTION # 25
A data engineer has a Delta table containing old data files that are no longer referenced after several update and delete operations. To remove these unused files and reclaim storage space, which Delta Lake command should be executed?
Answer: A
NEW QUESTION # 26
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?
Answer: B
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. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table
NEW QUESTION # 27
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?
Answer: D
Explanation:
Explanation
Temp view : session based Create temp view view_name as query All these are termed as session ended:
Opening a new notebook Detaching and reattaching a cluster Installing a python package Restarting a cluster
NEW QUESTION # 28
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?
Answer: B
NEW QUESTION # 29
A pipeline uses COPY INTO to ingest CSV files from cloud object storage into a Unity Catalog Delta table. Some files are occasionally re-uploaded with corrections using the same filename.
The engineer needs the corrected data to be ingested as soon as it becomes available.
What should the engineer do?
Answer: A
Explanation:
COPY INTO is idempotent by default and tracks files that have already been loaded into the target table. A corrected file uploaded under the same name and path can therefore be treated as previously processed and skipped. The reliable pattern is to publish the correction under a new unique filename so that ingestion detects it as a new file. The pipeline can then use a business key with MERGE, UPDATE, or equivalent transformation logic to apply the corrected values to the target Delta table. Overwriting or recreating the complete table risks unnecessary data loss, increases processing cost, and disrupts downstream consumers. Reloading every historical source file is also inefficient and can introduce duplicates. Therefore, option B supports incremental ingestion while applying corrections safely and efficiently.
NEW QUESTION # 30
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