Databricks Machine Learning Associate Question 384
Select 3You are training a linear regression model on a large dataset using Apache Spark in a Databricks notebook. How does Spark scale linear regression computations to handle such large datasets?
- A
Spark partitions the dataset across multiple nodes, and each node performs computations on its partition in parallel.
- B
Linear regression in Spark leverages distributed gradient descent algorithms that scale computations across the cluster.
- C
Spark uses a single driver node to perform all computations, ensuring consistency in the results.
- D
Spark optimizes linear regression training by caching intermediate data in memory to avoid recomputation.
- E
Spark requires datasets to fit entirely into the memory of a single node for linear regression to work.
Show answer and explanation
Correct answers: A, B, D
Explanation
Spark scales linear regression by distributing data and computations across a cluster of nodes. It partitions the dataset and uses distributed algorithms like gradient descent, enabling parallel processing. Additionally, Spark optimizes performance by caching intermediate data in memory, reducing redundant computation. This design allows Spark to handle datasets that are too large for a single machine's memory, making it well-suited for scalable machine learning tasks.
- A. Correct.
Correct: Spark splits the data into partitions, distributing them across nodes in the cluster. This allows each node to process its partition in parallel, enabling scalability for large datasets.
- B. Correct.
Correct: Spark's MLlib library uses distributed algorithms such as Stochastic Gradient Descent (SGD), which are designed to scale across a cluster for large-scale linear regression.
- C. Incorrect.
Incorrect: Spark does not rely on a single driver node for computations. The driver node coordinates tasks, but the actual computations are distributed across worker nodes.
- D. Correct.
Correct: Spark improves efficiency by caching intermediate results in memory when possible, reducing the need to recompute values during iterative algorithms like those used in linear regression.
- E. Incorrect.
Incorrect: Spark can handle datasets larger than the memory of a single node by partitioning the data and storing it across the cluster. It does not require the entire dataset to fit into the memory of a single node.