Databricks Machine Learning Associate Question 271
Single answerYou are tasked with building a scalable machine learning pipeline on a large distributed dataset using Databricks. Your goal is to implement a regression model and ensure that the training process can handle distributed computation efficiently. Which of the following libraries would be most suitable for this task?
- A
Spark ML
- B
scikit-learn
- C
TensorFlow
- D
NumPy
Show answer and explanation
Correct answer: A
Explanation
Spark ML is the key library for implementing traditional machine learning models in a distributed computing environment such as Databricks. It provides a scalable and efficient solution for handling large datasets across multiple nodes, making it the most suitable choice for this scenario.
- A. Correct.
Spark ML is specifically designed for distributed machine learning tasks in Apache Spark and is optimized for handling large datasets in a scalable manner.
- B. Incorrect.
scikit-learn is a powerful library for machine learning but it is not designed for distributed computation and is better suited for smaller, single-node datasets.
- C. Incorrect.
TensorFlow is a highly capable deep learning library but it is primarily focused on neural networks and not traditional machine learning pipelines for distributed systems.
- D. Incorrect.
NumPy is a numerical computing library that lacks built-in machine learning capabilities or distributed computation support, making it unsuitable for this task.