Databricks Machine Learning Associate Question 214
Single answerYou are training a machine learning model on a large dataset using a Databricks cluster. During training, you notice that adding more worker nodes to the cluster does not significantly reduce the training time. What could be the most likely reason for this behavior, and how should you address it?
- A
The dataset is too small, and the overhead of parallelization outweighs the benefits of additional compute resources.
- B
The cluster is running out of memory, causing excessive disk I/O and slowing down the training process.
- C
The model's training process is not optimized for distributed computing and should be rewritten to leverage Spark MLlib.
- D
The number of partitions in the dataset is too high, leading to excessive shuffling and reduced compute efficiency.
Show answer and explanation
Correct answer: A
Explanation
When training a machine learning model on a Databricks cluster, it is essential to manage the balance between dataset size, compute resources, and parallelization. If the dataset is too small, the parallelization overhead can negate the benefits of additional compute resources, leading to diminishing returns. In this case, optimizing resource allocation or processing the dataset on fewer nodes is often a better approach.
- A. Correct.
When the dataset is small, the overhead of managing parallelization across multiple nodes (communication, coordination, etc.) can outweigh the benefits of having additional compute resources. This is likely the issue here.
- B. Incorrect.
While memory issues can slow down training, the question does not mention any indications of memory-related bottlenecks, such as disk I/O spikes or error messages related to memory.
- C. Incorrect.
The question does not suggest that the model's code is inherently incompatible with distributed computing. Additionally, Spark MLlib is not a requirement for distributed training in Databricks.
- D. Incorrect.
While excessive partitions can lead to inefficiencies, this issue is more likely to cause performance degradation during data processing rather than during the actual model training phase. No information in the question indicates excessive partitioning.