Databricks Machine Learning Associate Question 7
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 better performance. Which type of Databricks cluster should you use to optimize the training process, and why?
- A
A standard cluster to leverage distributed computation across multiple worker nodes.
- B
A single-node cluster to avoid the overhead of distributed computation.
- C
A single-node cluster to ensure all data is stored and processed on a single machine.
- D
A standard cluster to reduce the cost of computation by using only the driver node.
Show answer and explanation
Correct answer: A
Explanation
A standard cluster is preferred when working with large datasets that require distributed computation, as it can parallelize processing across multiple worker nodes. This improves performance and scalability. A single-node cluster, on the other hand, is suitable for smaller workloads or debugging, where distributed computation is unnecessary or would add unnecessary overhead.
- A. Correct.
Correct: A standard cluster allows you to utilize multiple worker nodes, which is essential for training models on large datasets using distributed computation techniques.
- B. Incorrect.
Incorrect: While a single-node cluster avoids distributed computation overhead, it would not be appropriate for large datasets that require parallel processing to reduce computation time.
- C. Incorrect.
Incorrect: A single-node cluster ensures all data is stored and processed on a single machine, but this limits scalability and performance for large datasets.
- D. Incorrect.
Incorrect: A standard cluster does not reduce the cost of computation by using only the driver node; instead, it is designed to distribute computation across multiple nodes.