Databricks Machine Learning Associate Question 213
Select 2You are tasked with parallelizing a sequential machine learning model training process on Databricks. Why might this be challenging, particularly when the model involves iterative updates?
- A
The intermediate states of the model depend on the results of previous iterations.
- B
Iterative models often require synchronization between parallel tasks, which can introduce significant overhead.
- C
Parallelizing sequential models requires specialized hardware that may not be available in cloud environments.
- D
Many machine learning frameworks, including Databricks, do not support distributed training for iterative models.
- E
The computational cost of splitting data across distributed nodes outweighs the benefits of parallelization for such models.
Show answer and explanation
Correct answers: A, B
Explanation
Parallelizing sequential or iterative models is challenging because their structure inherently relies on sequential dependencies, where each iteration depends on the results of the previous one. Additionally, synchronization requirements between parallel tasks can introduce delays, reducing the efficiency of parallel execution. These challenges are not due to hardware limitations or lack of support in frameworks like Databricks, but rather the inherent nature of the iterative modeling process.
- A. Correct.
Correct: Sequential or iterative models rely on the results of previous steps, making it difficult to split the model's computations across parallel tasks.
- B. Correct.
Correct: Synchronization between parallel tasks is often required in iterative models, which can cause delays and reduce the benefits of parallelization.
- C. Incorrect.
Incorrect: Parallelization does not necessarily require specialized hardware; cloud platforms like Databricks offer resources for distributed processing.
- D. Incorrect.
Incorrect: Databricks supports distributed training for many types of models, though challenges exist for iterative ones due to their inherent dependencies.
- E. Incorrect.
Incorrect: While there is overhead associated with data distribution, it does not generally outweigh the benefits of parallelization in iterative models.