Databricks Machine Learning Associate Question 362
Single answerYou are tasked with processing a very large dataset in Databricks and performing custom transformations using a Python UDF. Why would you choose an iterator UDF over a standard UDF for this task?
- A
Iterator UDFs reduce the overhead of repeatedly invoking the Python interpreter by processing batches of data.
- B
Iterator UDFs guarantee faster execution by running the UDF logic directly on the Spark driver.
- C
Iterator UDFs allow the processing of data in smaller chunks, reducing memory pressure on the executors.
- D
Iterator UDFs automatically distribute the data evenly across all worker nodes, improving parallelism.
Show answer and explanation
Correct answer: A
Explanation
Iterator UDFs are preferred for large datasets because they process data in batches, reducing the overhead of frequent Python interpreter invocation. This makes them more efficient compared to standard UDFs, especially when working with large-scale data in distributed systems like Spark.
- A. Correct.
Correct: Iterator UDFs reduce the overhead of invoking the Python interpreter repeatedly by processing batches of data at once. This minimizes the cost associated with frequent interpreter calls, which is particularly beneficial for large datasets.
- B. Incorrect.
Incorrect: Iterator UDFs do not execute logic on the Spark driver. They operate on Spark executors, which are responsible for distributed data processing.
- C. Incorrect.
Incorrect: Iterator UDFs process data in batches, but they do not inherently reduce memory pressure. Memory management depends on the size of the batches and the available resources.
- D. Incorrect.
Incorrect: Data distribution and parallelism are managed by Spark's execution engine, not by the use of iterator UDFs.