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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Exam Format: | Proctored, Multiple choice |
| Available Languages: | Korean, English, Portuguese (Brazil), Japanese |
| Exam Price: | 200 USD |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Analyst Associate Databricks Certified Data Engineer Professional |
| Real Exam Qty: | 45 |
| Passing Score: | Not publicly disclosed, approximately 70% |
| Exam Duration: | 90 minutes |
| Recommended Training: | Databricks Documentation Databricks Academy - Data Engineer Associate Learning Path |
| Exam Registration: | Official Registration |
| Sample Questions: | Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | No mandatory prerequisites; 6+ months hands-on experience with Databricks and data engineering tasks recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a rigorous exam that tests candidates on their practical skills and knowledge of data engineering. Candidates will be required to complete a series of multiple-choice questions and hands-on tasks that simulate real-world data engineering scenarios. Databricks-Certified-Data-Engineer-Associate Exam is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines.
NEW QUESTION # 197
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?
Answer: B
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.
References:Databricks documentation on Delta Live Tables: Delta Live Tables Guide
NEW QUESTION # 198
A data engineer is designing a medallion architecture. Raw Kafka events must be stored exactly as received for reprocessing, then cleaned and deduplicated, then aggregated for BI dashboards.
Which mapping of layers is correct?
Answer: A
Explanation:
In the medallion architecture, bronze holds raw, append-only ingested data to enable full reprocessing; silver holds validated, deduplicated, and joined data; gold holds curated, aggregated datasets optimized for analytics and reporting.
NEW QUESTION # 199
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: A
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. Reference: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table
NEW QUESTION # 200
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
Answer: E
Explanation:
Explanation
In the classic Databricks architecture, the control plane includes components like the Databricks web application, the Databricks REST API, and the Databricks Workspace. These components are responsible for managing and controlling the Databricks environment, including cluster provisioning, notebook management, access control, and job scheduling. The other options, such as worker nodes, JDBC data sources, Databricks Filesystem (DBFS), and driver nodes, are typically part of the data plane or the execution environment, which is separate from the control plane. Worker nodes are responsible for executing tasks and computations, JDBC data sources are used to connect to external databases, DBFS is a distributed file system for data storage, and driver nodes are responsible for coordinating the execution of Spark jobs.
NEW QUESTION # 201
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing.
Which role must be in place to perform these actions across the metastore?
Answer: D
Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore. They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.
Reference Source: Databricks Unity Catalog Administration Guide - "Metastore admin privileges and ownership transfer."
NEW QUESTION # 202
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