Databricks Machine Learning Associate Question 8
Single answerYou are tasked with training a large machine learning model using a distributed dataset in Databricks. The dataset is stored in a distributed file system, and the training process requires significant parallel processing to optimize runtime. Which type of cluster would be most appropriate for this task?
- A
A single-node cluster because it simplifies configuration and avoids the overhead of distributed computing.
- B
A single-node cluster because it can handle distributed datasets efficiently without requiring multiple worker nodes.
- C
A standard cluster because it supports distributed processing across multiple nodes, which is suitable for large datasets.
- D
A standard cluster because it is more cost-effective for small datasets that require minimal computational power.
Show answer and explanation
Correct answer: C
Explanation
Standard clusters in Databricks are specifically designed for distributed processing tasks and can utilize multiple nodes to handle large datasets efficiently. In contrast, single-node clusters are limited to a single machine and are more appropriate for tasks that do not require distributed computation, such as small-scale development or debugging workflows. For training a large machine learning model on a distributed dataset, a standard cluster is the optimal choice.
- A. Incorrect.
This is incorrect because a single-node cluster cannot distribute the load across multiple nodes, making it unsuitable for large-scale data processing.
- B. Incorrect.
This is incorrect because single-node clusters are not designed to handle distributed datasets efficiently when significant parallel processing is required.
- C. Correct.
This is correct because a standard cluster can leverage multiple nodes to process large datasets in parallel, making it ideal for distributed computing tasks like training large machine learning models.
- D. Incorrect.
This is incorrect because standard clusters are generally more suitable for large-scale tasks requiring distributed processing, not for small datasets with minimal computational needs.