Databricks Machine Learning Associate Question 270
Select 3You are working with a large dataset in Databricks and need to build a distributed machine learning pipeline for a classification problem. Which of the following features of Spark ML make it suitable for this task?
- A
Spark ML provides distributed implementations of machine learning algorithms, enabling large-scale model training.
- B
Spark ML automatically tunes hyperparameters for all machine learning models without requiring user intervention.
- C
Spark ML integrates seamlessly with Spark DataFrames, allowing for efficient data preprocessing and feature engineering on distributed data.
- D
Spark ML supports exporting models to common formats like MLlib PMML for interoperability with other platforms.
- E
Spark ML provides a unified pipeline API to chain tasks like feature extraction, model training, and evaluation in a distributed manner.
Show answer and explanation
Correct answers: A, C, E
Explanation
Spark ML is a core library in Databricks for building distributed machine learning workflows. Its distributed nature, integration with Spark DataFrames, and unified pipeline API make it ideal for handling large-scale datasets and automating tasks like feature engineering, model training, and evaluation. However, it does not automatically tune hyperparameters or export models to PMML, which are outside its primary scope.
- A. Correct.
Correct. Spark ML is designed for distributed machine learning, which allows it to handle large datasets efficiently across a cluster.
- B. Incorrect.
Incorrect. While Spark ML supports some parameter tuning techniques, it does not automatically tune all hyperparameters without user intervention.
- C. Correct.
Correct. Spark ML works directly with Spark DataFrames, which is essential for scalable data preprocessing and feature engineering.
- D. Incorrect.
Incorrect. Spark ML does not natively support exporting models to PMML. It uses other mechanisms for model persistence, such as saving models in Spark-specific formats.
- E. Correct.
Correct. Spark ML's pipeline API simplifies the process of creating end-to-end machine learning workflows in a distributed environment.