Databricks Machine Learning Associate Question 390
Select 3You are training a large-scale random forest model using PySpark's MLlib on a distributed cluster. Which of the following characteristics correctly describe how Spark scales decision trees and ensembles like random forests?
- A
Decision tree training is parallelized by dividing the workload of evaluating splits across worker nodes.
- B
Each tree in a random forest is trained independently on a subset of data, allowing parallel computation.
- C
Spark scales decision trees by replicating the entire dataset on all worker nodes to ensure data locality.
- D
Random forests in Spark use data shuffling to ensure that every worker node processes the same data for identical trees.
- E
The ensemble model's predictions are aggregated from individual trees, which can be performed in parallel across a cluster.
Show answer and explanation
Correct answers: A, B, E
Explanation
Spark scales decision trees and ensembles like random forests by leveraging distributed computation. For decision trees, Spark parallelizes tasks such as evaluating feature splits across worker nodes. In random forests, each tree is trained independently on a bootstrapped subset of the data, enabling parallel training. Furthermore, ensemble predictions are aggregated from individual trees in parallel to optimize performance. Spark's distributed architecture avoids replicating the entire dataset across workers and instead partitions the data to maximize resource utilization.
- A. Correct.
Correct: Spark parallelizes decision tree training by splitting the task of evaluating possible feature splits across worker nodes, leveraging distributed computation.
- B. Correct.
Correct: Each tree in a random forest is trained independently on a different subset of data (using bootstrapping), which inherently allows parallel training across workers in Spark.
- C. Incorrect.
Incorrect: Spark does not replicate the entire dataset on all worker nodes. Instead, it partitions the data across the cluster to achieve distributed processing.
- D. Incorrect.
Incorrect: Random forests do not rely on data shuffling to process identical data on all nodes. Instead, they train trees on different subsets of the data (bootstrapped samples).
- E. Correct.
Correct: The final predictions of an ensemble model like a random forest are obtained by aggregating the predictions of individual trees, a process that can be distributed across the cluster for efficiency.