Databricks Machine Learning Associate Question 217
Single answerYou are training a machine learning model on Databricks and notice that increasing the number of worker nodes in your cluster does not improve the training speed. Instead, the overall runtime increases. What is the most likely reason for this behavior?
- A
The dataset is too small to benefit from additional parallelization.
- B
The model being trained does not support distributed training.
- C
The cluster is exceeding the Databricks compute resource limits for the workspace.
- D
The Databricks Runtime version is outdated and limits parallelization.
Show answer and explanation
Correct answer: A
Explanation
The balance between compute resources and parallelization depends on the size and complexity of the dataset. For very small datasets, the overhead introduced by parallelization can outweigh the performance gains from additional compute resources. This is why increasing the number of worker nodes may lead to slower training speeds in such scenarios.
- A. Correct.
If the dataset is too small, the overhead of managing parallelization across multiple nodes can exceed the benefits, leading to slower performance.
- B. Incorrect.
While some models may not support distributed training, this would typically result in the model running on a single node without negatively impacting performance for a small dataset.
- C. Incorrect.
Exceeding Databricks compute resource limits would typically result in errors or job failures, not slower performance.
- D. Incorrect.
An outdated Databricks Runtime might limit certain features or optimizations, but it would not directly explain slower performance due to additional worker nodes.