Databricks Machine Learning Associate Question 269
Single answerA data science team is tasked with building a scalable machine learning pipeline to predict customer churn using a large dataset stored in a distributed environment. They want to preprocess the data, train a logistic regression model, and evaluate the results. Why should the team use Spark ML for this task?
- A
Spark ML provides distributed processing capabilities, making it efficient for large-scale machine learning tasks.
- B
Spark ML is specifically designed to work only with deep learning models, enabling advanced feature extraction.
- C
Spark ML can only process data stored in databases, limiting its flexibility with other data storage options.
- D
Spark ML allows for seamless integration with Spark's DataFrame API, simplifying the handling of large-scale data.
Show answer and explanation
Correct answer: A
Explanation
Spark ML is a key library for distributed machine learning in Apache Spark. It provides distributed processing capabilities, enabling efficient handling of large-scale datasets. This makes it particularly useful for building scalable pipelines that involve preprocessing, model training, and evaluation. The integration with Spark's DataFrame API further simplifies data handling but is not the primary reason to choose Spark ML for distributed workloads.
- A. Correct.
Correct. Spark ML is built for distributed machine learning tasks, making it ideal for handling large datasets efficiently.
- B. Incorrect.
Incorrect. Spark ML is not limited to deep learning; it supports a wide range of traditional machine learning algorithms such as regression, classification, and clustering.
- C. Incorrect.
Incorrect. Spark ML can handle data from various sources, including file systems and streaming data, not just databases.
- D. Incorrect.
Incorrect. While Spark ML does integrate with Spark's DataFrame API, this alone is not the primary reason it is ideal for large-scale machine learning tasks.