Databricks Machine Learning Associate Question 210
Single answerYou are training a sequential machine learning model, such as an ARIMA time series model, on a large dataset in a distributed environment like Databricks. Why might it be challenging to parallelize the training process?
- A
Sequential models have dependencies between steps, requiring the output of one step to be available before the next can begin.
- B
Distributed environments like Databricks do not support parallel computations for any machine learning models.
- C
Parallelizing sequential models increases the risk of overfitting the training data, leading to poor generalization.
- D
Sequential models are inherently non-deterministic, which makes their training unsuitable for parallelization.
Show answer and explanation
Correct answer: A
Explanation
The difficulty in parallelizing sequential models stems from their stepwise dependency structure. Each step in the training process relies on the output of the previous step, making it inherently challenging to distribute the computation across multiple nodes or processes. While distributed environments like Databricks excel at parallelizing independent tasks, sequential dependencies limit the effectiveness of such parallelization.
- A. Correct.
Correct. Sequential models, by definition, have dependencies between steps. For example, in ARIMA, future predictions depend on past observations, making it difficult to parallelize the training process.
- B. Incorrect.
Incorrect. Databricks supports parallel computations for many machine learning models, but sequential models pose challenges due to their inherent structure, not due to limitations of the platform.
- C. Incorrect.
Incorrect. Parallelization does not inherently lead to overfitting. Overfitting is generally influenced by the model's complexity and training data, not the parallelization process.
- D. Incorrect.
Incorrect. Sequential models are deterministic in their training process. The challenge lies in their step-by-step dependency, not in non-determinism.