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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data pipelines and data solutions on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam is intended for professionals who have a strong understanding of data engineering concepts and have experience working with Databricks.
Databricks-Certified-Data-Engineer-Associate certification exam is designed to test and validate the skills and knowledge of data engineers who work with Databricks. Databricks is a cloud-based data processing and analytics platform that allows companies to process and analyze large amounts of data in real-time. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics, including data engineering, data processing, and machine learning.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q126-Q131):
NEW QUESTION # 126
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
- A. Workspace administrator
- B. This transfer is not possible
- C. New lead data engineer
- D. Databricks account representative
- E. Original data engineer
Answer: C
NEW QUESTION # 127
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?
- A. Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle
- B. Foreign tables federated from an external catalog to delegate optimization to the source system
- C. Temporary views over files to avoid table-level governance and lifecycle coupling
- D. External tables with a LOCATION pointing to an external volume for full control of file layout
Answer: A
Explanation:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly.
External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
=========
NEW QUESTION # 128
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)
- A. SELECT * FROM students FROM HISTORY VERSION AS OF 3
- B. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'
- C. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'
- D. SELECT * FROM students VERSION AS OF 5
- E. SELECT * FROM students@v4
Answer: B,E
NEW QUESTION # 129
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which change will need to be made to the pipeline when migrating to Delta Live Tables?
- A. The pipeline can have different notebook sources in SQL & Python.
- B. The pipeline will need to be written entirely in SQL.
- C. The pipeline will need to be written entirely in Python.
- D. The pipeline will need to use a batch source in place of a streaming source.
Answer: A
Explanation:
When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide
NEW QUESTION # 130
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. Repos
- C. Jobs
- D. Data Explorer
- E. Dashboards
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. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]
NEW QUESTION # 131
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