Databricks Machine Learning Associate Question 276
Select 3A data science team is tasked with building a machine learning model on a dataset that fits in memory on a single machine. They are considering using either scikit-learn or Spark ML for this task. Which of the following statements accurately describe the characteristics of scikit-learn relative to Spark ML?
- A
scikit-learn is optimized for single-node, in-memory computations, making it suitable for smaller datasets.
- B
scikit-learn can distribute computations across multiple nodes, making it more scalable than Spark ML.
- C
scikit-learn provides simpler APIs and is often easier to use for prototyping compared to Spark ML.
- D
Spark ML is better suited for handling distributed computations on large datasets than scikit-learn.
- E
scikit-learn requires a distributed cluster setup to run machine learning algorithms.
Show answer and explanation
Correct answers: A, C, D
Explanation
scikit-learn is a single-node, in-memory machine learning library that is well-suited for smaller datasets and prototyping due to its simplicity and ease of use. However, it lacks support for distributed computing, which is a key feature of Spark ML. Spark ML is therefore preferred when working with large datasets that exceed the memory limits of a single machine or require distributed processing.
- A. Correct.
Correct. scikit-learn is designed for single-node, in-memory computations, which makes it ideal for smaller datasets that fit entirely in RAM.
- B. Incorrect.
Incorrect. scikit-learn does not support distributed computations. It is limited to single-node processing, whereas Spark ML is designed for distributed environments.
- C. Correct.
Correct. scikit-learn offers user-friendly APIs and is commonly used for rapid prototyping of machine learning models on smaller datasets.
- D. Correct.
Correct. Spark ML is specifically built for distributed computing and is better suited for large datasets that cannot fit in memory on a single machine.
- E. Incorrect.
Incorrect. scikit-learn does not require a distributed cluster setup. It operates on a single node and is not designed for distributed computations.