Databricks Machine Learning Associate Question 274
Select 2A data scientist is working with a dataset that comfortably fits into the memory of a single machine. The data scientist is considering whether to use scikit-learn or Spark ML for model training. Which of the following statements correctly describe scikit-learn as a solution in this scenario?
- A
Scikit-learn is well-suited for single-node environments where the dataset fits into memory.
- B
Scikit-learn can distribute computations across a cluster, making it ideal for large-scale datasets.
- C
Scikit-learn provides a simpler API compared to Spark ML for single-node machine learning tasks.
- D
Scikit-learn is optimized for distributed machine learning workflows across multiple nodes.
Show answer and explanation
Correct answers: A, C
Explanation
Scikit-learn is a single-node solution that is effective for machine learning tasks when the dataset fits into memory. It provides a simpler API compared to Spark ML, which is designed for distributed machine learning workflows. Unlike Spark ML, scikit-learn cannot distribute computations across a cluster, making it less suitable for very large datasets or workflows requiring distributed processing.
- A. Correct.
Scikit-learn is designed for single-node environments and works well when the dataset fits into memory, making it a suitable choice in this scenario.
- B. Incorrect.
This is incorrect because scikit-learn does not have native support for distributed computations across a cluster. Spark ML is better suited for such use cases.
- C. Correct.
Scikit-learn's API is simpler and more intuitive for single-node machine learning tasks compared to Spark ML, which is optimized for distributed systems.
- D. Incorrect.
This is incorrect because scikit-learn is not optimized for distributed machine learning; it operates on a single node and doesn't leverage multiple nodes in a cluster.