Databricks Machine Learning Associate Question 383
Select 3You are working with a large dataset containing millions of rows and want to fit a linear regression model using Apache Spark's MLlib. Which of the following statements accurately describe how Spark scales linear regression?
- A
Spark uses distributed data processing to split the dataset across multiple nodes, enabling efficient computation for large-scale data.
- B
Spark's linear regression implementation supports both batch gradient descent and stochastic gradient descent, which can run in parallel across nodes.
- C
Spark leverages distributed matrix operations to calculate coefficients efficiently, reducing the computational bottleneck of solving linear systems.
- D
Spark automatically tunes hyperparameters for linear regression models to ensure optimal performance on distributed systems.
- E
Spark's linear regression implementation can leverage iterative algorithms like L-BFGS, which are optimized for distributed systems.
Show answer and explanation
Correct answers: A, C, E
Explanation
Spark scales linear regression by distributing data and computations across a cluster, leveraging parallel processing and optimized iterative algorithms like L-BFGS. It does not natively support stochastic gradient descent for linear regression, nor does it automatically tune hyperparameters, requiring user intervention for model optimization.
- A. Correct.
Correct. Spark scales linear regression by distributing the data across nodes and using parallel computation to handle large datasets efficiently.
- B. Incorrect.
Incorrect. While Spark's MLlib implements some gradient-based optimization techniques, it does not support stochastic gradient descent (SGD) for linear regression natively.
- C. Correct.
Correct. Spark's distributed computation engine efficiently handles matrix operations required for solving linear regression problems, which are computationally intensive for large datasets.
- D. Incorrect.
Incorrect. Spark does not automatically tune hyperparameters for linear regression models. Hyperparameter tuning must be performed explicitly by the user, often using tools like cross-validation.
- E. Correct.
Correct. Spark's MLlib can use algorithms like L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno), which are iterative and optimized for distributed systems to compute linear regression coefficients.