Databricks Machine Learning Associate Question 218
Single answerYou are training a machine learning model on a large dataset using Databricks. During training, you notice that increasing the number of worker nodes beyond a certain point does not improve performance and may even slow down the process. What is the MOST likely reason for this behavior, and how should you address it?
- A
The dataset is not large enough to fully utilize the additional compute resources, so parallelization overhead becomes significant.
- B
The training process is bottlenecked by a single worker node that has insufficient compute power.
- C
The Spark cluster is not configured to use all available worker nodes for model training.
- D
The model training algorithm does not support parallelization, so additional workers are unused.
Show answer and explanation
Correct answer: A
Explanation
Adding more compute resources to a task does not always guarantee performance improvements. When the dataset is relatively small or the workload is not evenly distributed, the overhead from parallelizing the task (e.g., communication and synchronization costs) can outweigh the benefits of additional resources. This is a common consideration when balancing compute resources and parallelization in distributed machine learning workflows.
- A. Correct.
Correct: When the dataset is small or the workload is not evenly distributed, adding more compute resources can result in significant parallelization overhead, such as increased communication or synchronization costs, which slows down training.
- B. Incorrect.
Incorrect: While a single worker bottleneck could impact performance, it is less likely to explain the specific behavior described in the question, as the issue arises from adding more workers, not a single node's limitation.
- C. Incorrect.
Incorrect: If the Spark cluster were misconfigured, it would likely result in underutilization of resources from the start, rather than a performance decrease when adding more workers.
- D. Incorrect.
Incorrect: Most modern machine learning algorithms in Databricks support some level of parallelization. The issue here is related to diminishing returns from additional compute resources, not a lack of parallelization support.