Databricks Machine Learning Associate Question 212
Single answerYou are tasked with training a sequential machine learning model, such as a time-series forecasting model, on a large dataset. A team member suggests parallelizing the training process to speed up computation. Why might this approach be inherently difficult to implement?
- A
Sequential models rely on the output of previous steps, which creates dependencies that cannot be easily parallelized.
- B
Parallelizing sequential models often leads to poor model accuracy due to lack of regularization.
- C
Sequential models require more memory and storage, which makes parallelization computationally expensive.
- D
Parallelization works best for models with independent computations, which is not the case for sequential models.
Show answer and explanation
Correct answer: A
Explanation
Parallelization is most effective when computations are independent and can be performed simultaneously. In sequential models, each step relies on the output of the previous step, creating a dependency chain that prevents simultaneous execution. This characteristic of sequential models is the primary reason why parallelizing them is inherently difficult.
- A. Correct.
Sequential models, such as those used in time-series forecasting or recurrent neural networks, depend on the output of previous steps for subsequent computations. This dependency chain makes it inherently challenging to parallelize.
- B. Incorrect.
While model accuracy can be affected by various factors, this is not a reason why parallelizing sequential models is inherently difficult. The issue lies in the dependency structure of the computations.
- C. Incorrect.
Memory and storage requirements are not the primary reason for the difficulty in parallelizing sequential models. The challenge arises from the dependency on prior outputs rather than computational resource limitations.
- D. Incorrect.
This statement is true, but it is a more general observation rather than the specific reason why sequential models are difficult to parallelize. It does not directly address the core issue of dependency within sequential models.