Databricks Machine Learning Associate Question 211
Select 3In a distributed machine learning environment, why is it challenging to parallelize sequential or iterative models like Gradient Descent?
- A
Sequential models often rely on the output of one iteration to inform the next, making parallel execution difficult.
- B
Iterative models require high computational power, which is inherently incompatible with distributed systems.
- C
Stateful dependencies in iterative algorithms can introduce synchronization issues across distributed nodes.
- D
Parallelizing sequential models can lead to inaccuracies because distributed systems cannot handle floating-point operations.
- E
Some iterative models require global state updates, which can become a bottleneck in parallel processing.
Show answer and explanation
Correct answers: A, C, E
Explanation
Parallelizing sequential or iterative models like Gradient Descent is challenging because they rely on step-by-step updates where the output of one iteration directly impacts the next. These dependencies create issues in distributed systems, as synchronizing state across nodes and managing global updates can become bottlenecks. The difficulty lies not in computational power or precision, but in maintaining consistency and dependency order in a distributed environment.
- A. Correct.
Correct. Sequential models like Gradient Descent use the output of one iteration to update the model for the next iteration, which inherently limits parallelization.
- B. Incorrect.
Incorrect. While iterative models may require significant computational resources, distributed systems are designed to address such challenges. The difficulty lies in managing dependencies, not computational power.
- C. Correct.
Correct. Iterative algorithms often have stateful dependencies, which can lead to challenges in synchronizing updates across distributed nodes.
- D. Incorrect.
Incorrect. Distributed systems can handle floating-point operations; the challenge lies in ensuring consistency and managing dependencies in iterative models, not the precision of computations.
- E. Correct.
Correct. Some iterative models require global state updates (like updating model weights), and coordinating these updates across distributed systems can introduce bottlenecks.