Databricks Machine Learning Associate Question 10
Select 2A data scientist is tasked with training a machine learning model on a relatively small dataset and performing hyperparameter tuning using a Python-based library. The dataset and computations can fit comfortably in the memory of a single machine. However, the data scientist also needs to run a distributed grid search on a larger dataset for another project. Which type of cluster should be used for each task?
- A
Use a single-node cluster for the small dataset training and hyperparameter tuning task.
- B
Use a standard cluster for the small dataset training and hyperparameter tuning task.
- C
Use a single-node cluster for the distributed grid search on the larger dataset.
- D
Use a standard cluster for the distributed grid search on the larger dataset.
Show answer and explanation
Correct answers: A, D
Explanation
Single-node clusters are suitable for tasks that are lightweight and can fit within the resources of a single machine, such as training on small datasets or running lightweight experiments. Standard clusters, on the other hand, are designed for distributed workloads such as processing large datasets or performing distributed machine learning tasks, where parallelism and scalability are necessary.
- A. Correct.
A single-node cluster is ideal for small datasets and computations that can fit into the memory of a single machine. It avoids the overhead of managing distributed nodes, making it more efficient for this task.
- B. Incorrect.
A standard cluster is unnecessary for the small dataset task because it introduces additional overhead related to distributed computing, which is not required in this scenario.
- C. Incorrect.
A single-node cluster is not suitable for distributed grid search on a large dataset because the task involves distributed processing, which a single-node cluster cannot handle efficiently.
- D. Correct.
A standard cluster is the correct choice for distributed grid search on a large dataset as it allows computations to be distributed across multiple nodes, enabling parallel processing and handling larger data volumes.