Databricks Machine Learning Associate Question 272
Single answerYou are working with a large dataset containing millions of rows and need to train a machine learning model for predicting customer churn. To handle the scale of the data and distribute the workload efficiently, which library in Databricks should you use, and why?
- A
Use Spark ML, as it provides distributed machine learning algorithms that leverage the power of Spark clusters.
- B
Use scikit-learn, as it is optimized for large-scale distributed computing tasks in Databricks.
- C
Use TensorFlow, as it is specifically designed for distributed traditional machine learning workflows in Spark.
- D
Use pandas, as it is capable of handling distributed machine learning workloads natively.
Show answer and explanation
Correct answer: A
Explanation
Spark ML is the key library within Databricks for distributing traditional machine learning workloads. It leverages the distributed computing capabilities of Spark clusters, making it ideal for handling large-scale datasets efficiently. Other libraries, such as scikit-learn or pandas, are not designed for distributed workloads, while TensorFlow is more suitable for deep learning rather than traditional machine learning.
- A. Correct.
Spark ML is the correct library for this scenario because it is designed to work on distributed systems and can handle large-scale machine learning tasks efficiently using Spark clusters.
- B. Incorrect.
scikit-learn is not optimized for distributed computing. While it is a powerful library for machine learning, it primarily works on single-node systems and is not suitable for large-scale distributed workloads like those handled by Spark ML.
- C. Incorrect.
TensorFlow is primarily used for deep learning tasks, not traditional machine learning workflows. It does not integrate natively with Spark for distributed traditional machine learning tasks.
- D. Incorrect.
pandas is a library for data manipulation and analysis, not for machine learning. It also does not support distributed machine learning workloads natively.