Databricks Machine Learning Associate Question 382
Single answerA data science team is implementing a linear regression model on a large dataset using Apache Spark. The dataset is too large to fit into the memory of a single machine. How does Spark scale linear regression to handle such large-scale data efficiently?
- A
By distributing the dataset across multiple nodes and performing computations in parallel
- B
By implementing linear regression as a single-threaded operation on a single node to ensure accuracy
- C
By using gradient descent optimization that leverages Spark's distributed processing capabilities
- D
By automatically down-sampling the dataset to fit into memory and then using traditional linear regression methods
Show answer and explanation
Correct answer: A
Explanation
Spark scales linear regression by distributing the dataset across multiple nodes in a cluster and executing operations in parallel. This distributed processing ensures that large datasets, which cannot fit into the memory of a single machine, can be handled efficiently. The distributed nature of Spark is the primary reason it can scale linear regression, not by reducing dataset size or relying solely on specific optimization methods.
- A. Correct.
Correct. Spark distributes the data across multiple worker nodes and performs computations in parallel, allowing it to scale efficiently for large datasets.
- B. Incorrect.
Incorrect. Spark does not execute linear regression as a single-threaded operation. Instead, it leverages distributed computing to handle large-scale data.
- C. Incorrect.
Incorrect. While gradient descent can be used for optimization, it is not the fundamental reason Spark scales linear regression. The key reason is Spark's distributed nature.
- D. Incorrect.
Incorrect. Spark does not down-sample the dataset; it processes the entire dataset by distributing it across nodes, ensuring scalability.