Databricks Machine Learning Associate Question 9
Single answerYou are tasked with training a machine learning model on a large dataset stored in a distributed file system. The training process involves parallelizing computations across multiple nodes for efficiency. Which type of cluster should you choose in this scenario, and why?
- A
A standard cluster, because it allows distributed processing across multiple nodes.
- B
A single-node cluster, because it is optimized for large-scale distributed processing.
- C
A standard cluster, because it is specifically designed for single-node computations.
- D
A single-node cluster, because it is more cost-effective for distributed computations.
Show answer and explanation
Correct answer: A
Explanation
A standard cluster is preferred for workloads that require distributed processing, such as training machine learning models on large datasets. It can parallelize computations across multiple worker nodes, significantly improving efficiency and scalability. In contrast, a single-node cluster is limited to computations on a single machine and is better suited for lightweight tasks or development and testing purposes.
- A. Correct.
This is correct. A standard cluster is ideal for distributed processing across multiple nodes, which is necessary for efficiently handling large datasets and parallelizing computations.
- B. Incorrect.
This is incorrect. A single-node cluster cannot perform distributed processing, as it runs all computations on a single machine. It is not suitable for large-scale distributed workloads.
- C. Incorrect.
This is incorrect. A standard cluster is not designed for single-node computations; it is intended for distributed workloads across multiple nodes.
- D. Incorrect.
This is incorrect. A single-node cluster is not suitable for distributed computations, regardless of cost; it runs all tasks on a single machine and cannot parallelize computations.