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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a comprehensive certification program designed to validate the skills and knowledge of data engineers in using Databricks to build and manage data pipelines, perform data analysis, and develop data-driven solutions. Databricks-Certified-Data-Engineer-Associate exam is designed for professionals who work with big data and are responsible for designing, building, and maintaining data pipelines using Databricks.

Becoming a Databricks Certified Data Engineer Associate provides several benefits to candidates. Firstly, it validates their skills and knowledge in data engineering, making them more competitive in the job market. Secondly, it demonstrates their commitment to staying current with the latest technologies and their dedication to professional development. Finally, it provides them with access to a community of Databricks-certified professionals, which can help them network and collaborate with other data engineers.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing.
Which strategy will reduce data egress costs associated with cross-cloud data sharing?

A. Sharing data via pre-signed URLs without monitoring egress costs
B. Configure VPN connection between AWS and Azure for faster data sharing
C. Migrating the dataset to Cloudflare R2 object storage before sharing
D. Using Delta Sharing without any additional configurations

Answer: D

NEW QUESTION # 64
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. USING DELTA
B. FROM "path/to/csv"
C. USING CSV
D. FROM CSV
E. None of these lines of code are needed to successfully complete the task

Answer: C

NEW QUESTION # 65
Which file format is used for storing Delta Lake Table?

A. SV
B. Delta
C. JSON
D. Parquet

Answer: D

NEW QUESTION # 66
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The OPTIMIZE command was nun on the table
B. The HISTORY command was run on the table
C. The VACUUM command was run on the table
D. The DELETE HISTORY command was run on the table
E. The TIME TRAVEL command was run on the table

Answer: C

Explanation:
Explanation
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUMwith certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.

NEW QUESTION # 67
A global retail company sells products across multiple categories (e.g.. Electronics, Clothing) and regions (e.
g.. North. South, East. West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

A. Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))
B. Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))
C. Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))
D. Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))

Answer: B

NEW QUESTION # 68
......

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