Databricks Data Engineer Associate Question 264
Select 2You are tasked with inspecting the Delta Lake files for a Delta table named sales_data stored in the /delta/sales directory. Which of the following approaches can help you inspect the directory structure to understand the Delta table's metadata and changes over time?
- A
Use the Databricks file system (DBFS) command
%fs ls /delta/salesto list the files in the root Delta table directory. - B
Query the Delta table using the
DESCRIBE DETAILSQL command to retrieve detailed metadata about the table including its location. - C
Inspect the
_delta_log/directory within/delta/salesto view JSON or checkpoint files that log transactional changes. - D
Read the Delta table using a PySpark
spark.read.format('delta').load('/delta/sales')command to inspect the physical files on disk. - E
Directly open the Parquet files in the
/delta/salesdirectory using a Parquet reader to understand Delta table metadata.
Show answer and explanation
Correct answers: A, C
Explanation
To inspect the directory structure of a Delta Lake table, you can use the %fs ls command to list the files and directories within the Delta table's root folder. Additionally, examining the _delta_log/ directory is essential as it contains the JSON and checkpoint files that record the Delta table's transactional history. These approaches provide insights into the organization and metadata of the Delta Lake files.
- A. Correct.
Correct. The
%fs lscommand can list the files in the root Delta table directory, allowing you to inspect the Delta table's structure and locate important subdirectories like_delta_log. - B. Incorrect.
Incorrect. The
DESCRIBE DETAILcommand provides metadata about the Delta table itself, such as its schema, location, and properties, but it does not directly inspect the file structure. - C. Correct.
Correct. The
_delta_log/directory contains JSON and checkpoint files that record the transactional history of the Delta table, and inspecting this directory is crucial for understanding the table's changes over time. - D. Incorrect.
Incorrect. While the
spark.read.format('delta').load()command can load the table for analysis, it does not provide visibility into the physical file structure or the log files. - E. Incorrect.
Incorrect. Parquet files in the Delta table are used for data storage but do not contain Delta-specific metadata or transactional history, so directly opening them is not helpful for inspecting the Delta Lake directory structure.