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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive examination that tests the skills of professionals who work with data on the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is designed to help professionals stay up-to-date with the latest data engineering trends and technologies, and it can help professionals advance their career in the field of data engineering. By earning this certification, professionals can demonstrate their commitment to professional development and their dedication to staying current in their field.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is designed for professionals who are interested in validating their expertise in building and managing big data processing systems using Databricks. Databricks is a unified data analytics platform that offers a cloud-based environment for processing big data workloads. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data engineering, data processing, data storage, and data analysis.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q285-Q290):

NEW QUESTION # 285
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. There is no way to indicate whether a table contains PII.
B. "COMMENT PII"
C. TBLPROPERTIES PII
D. COMMENT "Contains PII"
E. PII

Answer: D

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

NEW QUESTION # 286
A data engineer is building an ETL pipeline in Databricks that loads raw JSON files from cloud object storage into a Delta Lake table. The pipeline must ensure ACID transactions and schema enforcement while supporting scalable reads and writes from multiple concurrent jobs. Which storage format should the engineer use?

A. Avro
B. CSV
C. Delta Lake
D. Parquet

Answer: C

NEW QUESTION # 287
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger("5 seconds")
B. trigger(processingTime="5 seconds")
C. trigger(continuous="5 seconds")
D. trigger(once="5 seconds")

Answer: B

NEW QUESTION # 288
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: B

NEW QUESTION # 289
Which of the following describes the storage organization of a Delta table?

A. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
B. Delta tables are stored in a single file that contains only the data stored within the table.
C. Delta tables are stored in a collection of files that contain only the data stored within the table.
D. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
E. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.

Answer: A

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. References:
* What is Delta Lake? | Databricks on AWS
* Quickstart - Delta Lake Documentation

NEW QUESTION # 290
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