Databricks Databricks-Certified-Data-Engineer-Associate Certification Exam Infor | Databricks-Certified-Data-Engineer-Associate Valid Exam Test

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is a credential that certifies individuals in their ability to design, develop, and manage data pipelines using Databricks. It is a globally recognized certification that validates the skills and knowledge required to work with big data processing and analytics using Databricks.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q127-Q132):

NEW QUESTION # 127
Which of the following describes the relationship between Bronze tables and raw data?

A. Bronze tables contain a less refined view of data than raw data.
B. Bronze tables contain more truthful data than raw data.
C. Bronze tables contain aggregates while raw data is unaggregated.
D. Bronze tables contain raw data with a schema applied.
E. Bronze tables contain less data than raw data files.

Answer: D

Explanation:
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures "as-is", along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. Reference: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.

NEW QUESTION # 128
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog
B. Delta Live Tables
C. Delta Lake
D. Data Explorer
E. Auto Loader

Answer: C

NEW QUESTION # 129
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 location can the data engineer review their permissions on the table?

A. Catalog Explorer
B. Repos
C. Dashboards
D. Jobs

Answer: A

Explanation:
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html

NEW QUESTION # 130
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. The data engineer wants 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?

A. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
B. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
D. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.

Answer: A

NEW QUESTION # 131
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information. How should the data engineer achieve this using Databricks?

A. Export data from both sources to CSV files and upload them to Databricks
B. Develop custom ETL pipelines to ingest data into Databricks
C. Manually synchronize data from both sources into a single database
D. Use Lakehouse Federation to query both data sources directly

Answer: D

Explanation:
Lakehouse Federation allows Databricks to directly query external data sources like PostgreSQL and Azure Synapse without duplicating data. This ensures the report always uses up-to-date information while avoiding the overhead and cost of data movement.

NEW QUESTION # 132
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