Databricks Machine Learning Associate Question 358
Select 2You are working on a machine learning pipeline in Databricks and need to apply a user-defined function (UDF) to preprocess a large dataset stored in a Delta table. The dataset is too large to fit into memory. Why would you choose to use an iterator UDF instead of a standard UDF?
- A
Iterator UDFs are more memory-efficient because they process data in batches.
- B
Iterator UDFs are faster because they precompile the entire dataset before execution.
- C
Iterator UDFs can handle larger-than-memory datasets by streaming data through the function.
- D
Iterator UDFs automatically optimize the underlying Spark query plan.
Show answer and explanation
Correct answers: A, C
Explanation
Iterator UDFs are preferred for large datasets because they process data in an iterative, batch-wise manner, allowing them to handle datasets that are larger than the available memory. This is especially important in distributed systems like Spark, where memory efficiency is critical for scalability. Unlike standard UDFs, which may require the entire dataset to fit in memory, iterator UDFs stream data through the function, reducing memory pressure and enabling efficient processing.
- A. Correct.
Correct: Iterator UDFs process data in batches, which reduces memory overhead and makes them suitable for large datasets.
- B. Incorrect.
Incorrect: Iterator UDFs do not precompile the dataset; instead, they process data in chunks during execution.
- C. Correct.
Correct: Iterator UDFs are designed to handle large datasets by streaming data through the function, avoiding memory issues.
- D. Incorrect.
Incorrect: Iterator UDFs do not optimize the Spark query plan; query optimization is handled separately by Spark.