Databricks Generative AI Engineer Associate Question 62
Select 4You are working on a Databricks notebook that processes chunked text data in real-time. The data needs to be written into a Delta Lake table stored in Unity Catalog for long-term storage and analytics. Which of the following steps must you include to successfully write this data into the Delta Lake table?
- A
Define a Delta table using Unity Catalog by specifying the schema and table location.
- B
Use the spark.write.format('delta') API to write the processed data into the Delta table.
- C
Ensure that the Unity Catalog permissions for the Delta table are set to allow write access.
- D
Use the spark.read.format('delta') API to load the data into the Delta table.
- E
Partition the Delta Lake table to optimize for performance if the dataset is large and query patterns warrant it.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
To successfully write chunked text data into a Delta Lake table in Unity Catalog, you must define the Delta table with the correct schema and location, use the appropriate write API (e.g., spark.write.format('delta')), ensure proper access permissions in Unity Catalog, and consider partitioning if the dataset is large for performance optimization. Reading the data (e.g., with spark.read.format('delta')) is not part of the writing process and is therefore incorrect.
- A. Correct.
Defining the Delta table in Unity Catalog with the correct schema and location is essential to ensure the data is written to the correct table and location.
- B. Correct.
Using the spark.write.format('delta') API is the standard method to write data into Delta Lake tables in Databricks.
- C. Correct.
Setting proper Unity Catalog permissions is necessary to ensure that the notebook has the required access to write data into the Delta table.
- D. Incorrect.
The spark.read.format('delta') API is used to read Delta tables, not write to them, so this option is incorrect.
- E. Correct.
Partitioning the Delta Lake table for large datasets can improve performance and is an important step if the dataset size and query patterns justify it.