Databricks Machine Learning Associate Question 387
Single answerYou are training a machine learning model using Spark MLlib on a large dataset. You decide to use a Random Forest model, which is an ensemble of decision trees. How does Spark optimize the training of decision trees in a distributed environment?
- A
By distributing each individual decision tree across several worker nodes for parallel computation
- B
By training multiple decision trees independently on different subsets of data using worker nodes in parallel
- C
By splitting the dataset feature-wise and assigning different features to different workers for tree construction
- D
By creating a single decision tree on the driver node and broadcasting it to the workers for optimization
Show answer and explanation
Correct answer: B
Explanation
Spark scales decision tree training in Random Forests by leveraging data parallelism. Each tree in the ensemble is trained independently on a different data subset across worker nodes. This approach ensures efficient utilization of cluster resources and allows Spark to handle large-scale datasets effectively.
- A. Incorrect.
Incorrect. Spark does not distribute a single decision tree across multiple worker nodes. Each tree is trained independently on a subset of the data.
- B. Correct.
Correct. Spark parallelizes the training of decision trees by assigning different subsets of the data to different worker nodes, allowing independent training of each tree in the ensemble.
- C. Incorrect.
Incorrect. Spark does not split the dataset feature-wise for training decision trees. The data is split row-wise for parallel processing.
- D. Incorrect.
Incorrect. Spark does not train a single decision tree on the driver node. Instead, it distributes the work across worker nodes to ensure scalability.