Databricks Machine Learning Associate Question 391
Select 3You are training a Random Forest model using Spark MLlib on a large dataset that is distributed across multiple worker nodes in a Databricks cluster. How does Spark ensure the scalability of decision tree-based models like Random Forest during training?
- A
Each decision tree in the ensemble is trained independently on different subsets of data, leveraging data parallelism across Spark worker nodes.
- B
Spark uses lazy evaluation to only build decision trees when predictions are requested, reducing memory overhead.
- C
The training process for individual decision trees is parallelized by splitting computations across nodes for subsets of features and data.
- D
Spark automatically enforces sampling with replacement (bootstrapping) for each tree in the Random Forest, allowing distribution of data preparation across the cluster.
- E
The ensemble predictions are aggregated on the driver node, which collects the outputs of all decision trees and computes the final prediction.
Show answer and explanation
Correct answers: A, C, D
Explanation
Spark ensures the scalability of decision tree-based models like Random Forest by leveraging distributed computation across a cluster. Each tree is trained independently in parallel on different subsets of data, and data preparation steps like bootstrapping are also distributed. Additionally, Spark parallelizes computations for splits and features during training. These mechanisms allow Spark to handle large datasets efficiently, making it suitable for scaling machine learning models.
- A. Correct.
Correct: Spark trains each decision tree in the ensemble independently, taking advantage of data parallelism across worker nodes. This is a key way Spark scales decision tree-based models.
- B. Incorrect.
Incorrect: Lazy evaluation in Spark relates to the execution of transformations, not the training or building of decision trees. Decision trees are fully trained during the model training process.
- C. Correct.
Correct: Spark parallelizes the computation for individual decision trees by distributing data and feature splits across worker nodes. This ensures efficient model training on large datasets.
- D. Correct.
Correct: Bootstrapping (sampling with replacement) is automatically applied for Random Forests, and Spark distributes this process across cluster nodes to prepare data efficiently.
- E. Incorrect.
Incorrect: Ensemble predictions are aggregated in parallel and not solely on the driver node. Spark minimizes driver workload by performing as much computation as possible on worker nodes.