Databricks Machine Learning Associate Question 386
Select 3You are working on a machine learning pipeline in Databricks and need to scale linear regression training on a large dataset of several terabytes. How does Apache Spark handle this scaling efficiently?
- A
Spark uses distributed data processing by splitting the data into partitions and parallelizing computations.
- B
Spark trains multiple linear regression models simultaneously for different subsets of data and averages their coefficients.
- C
Spark leverages matrix factorization techniques implemented in MLlib to optimize distributed linear regression computations.
- D
Spark performs gradient descent in parallel by splitting the computation of gradients across worker nodes.
- E
Spark uses in-memory computation to reduce the overhead of reading and writing intermediate data during linear regression training.
Show answer and explanation
Correct answers: A, D, E
Explanation
Apache Spark efficiently scales linear regression by leveraging its distributed computing framework. It partitions the data across multiple nodes and parallelizes computation of gradients during optimization (e.g., gradient descent). Additionally, Spark's in-memory computation significantly improves processing speed by reducing disk I/O overhead. This combination of features allows Spark to handle large-scale datasets effectively.
- A. Correct.
Correct: Spark partitions the data and parallelizes computations across its distributed cluster architecture, making it possible to handle large datasets efficiently.
- B. Incorrect.
Incorrect: Spark does not train separate models for subsets of data and average their coefficients. Instead, it trains a single linear regression model across the entire dataset in a distributed fashion.
- C. Incorrect.
Incorrect: Spark does not specifically rely on matrix factorization for linear regression. While Spark MLlib uses optimized linear algebra libraries, matrix factorization is not central to scaling linear regression.
- D. Correct.
Correct: Spark distributes gradient computations across worker nodes during the iterative optimization process, such as gradient descent, enabling efficient parallelism.
- E. Correct.
Correct: Spark's in-memory computation reduces the overhead of disk I/O, which is critical for scaling large-scale machine learning tasks like linear regression.