Databricks Machine Learning Associate Question 359
Single answerYou are processing a large dataset in Databricks and need to apply a custom transformation function using a User-Defined Function (UDF). Why would you choose an iterator UDF over a standard scalar UDF for this task?
- A
Iterator UDFs process data in batches, reducing serialization and deserialization overhead.
- B
Iterator UDFs allow for parallel execution across multiple worker nodes automatically.
- C
Iterator UDFs support vectorized operations, which lead to faster computation on individual rows.
- D
Iterator UDFs enable processing of large datasets without exceeding memory limits on executors.
Show answer and explanation
Correct answer: A
Explanation
Iterator UDFs in Databricks are specifically designed to handle large datasets efficiently by processing data in batches. This reduces the computational overhead caused by frequent serialization and deserialization in scalar UDFs. This makes iterator UDFs the preferred choice for large-scale data transformations.
- A. Correct.
Correct: Iterator UDFs process data in batches, reducing the overhead of serializing and deserializing data row by row. This makes them efficient for large datasets.
- B. Incorrect.
Incorrect: While Databricks does distribute work across multiple nodes, this is independent of whether iterator UDFs or scalar UDFs are used.
- C. Incorrect.
Incorrect: Iterator UDFs do not inherently support vectorized operations. Vectorized operations are typically related to pandas or other similar libraries.
- D. Incorrect.
Incorrect: While iterator UDFs process data in batches, this reduces overhead but doesn't directly prevent memory limits from being exceeded.