Databricks Machine Learning Associate Question 273
Select 3You are working with a large dataset of customer transactions and want to build a logistic regression model to predict customer churn. The dataset is too large to fit into memory on a single machine. Which of the following reasons justify using Spark ML for this task?
- A
Spark ML provides distributed data processing, allowing the dataset to be processed across a cluster.
- B
Spark ML includes pre-built implementations of machine learning algorithms, such as logistic regression, that operate on distributed data.
- C
Spark ML automatically optimizes hyperparameters for machine learning models during training.
- D
Spark ML seamlessly integrates with the Databricks environment, enabling efficient collaboration and data workflows.
- E
Spark ML requires all data to be loaded into memory on a single machine, making it unsuitable for large datasets.
Show answer and explanation
Correct answers: A, B, D
Explanation
Spark ML is a key library for distributed machine learning in Spark. It provides scalable implementations of machine learning algorithms and integrates effectively with Databricks, making it ideal for handling large datasets that cannot fit into memory on a single machine. However, hyperparameter optimization is not automated within Spark ML, and users must implement it explicitly.
- A. Correct.
Correct: Spark ML's core advantage is its ability to distribute data processing across a cluster, making it suitable for large datasets that cannot fit into memory on a single machine.
- B. Correct.
Correct: Spark ML provides distributed implementations of common machine learning algorithms, such as logistic regression, that can handle distributed datasets efficiently.
- C. Incorrect.
Incorrect: Spark ML does not automatically optimize hyperparameters. Users need to use tools like cross-validation or hyperparameter tuning frameworks to perform this task.
- D. Correct.
Correct: Spark ML integrates well with the Databricks platform, facilitating large-scale machine learning workflows and collaborative development.
- E. Incorrect.
Incorrect: This statement is false. Spark ML is specifically designed to handle distributed data, making it suitable for large datasets.