Databricks Machine Learning Associate Question 277
Select 3A data scientist is working on a machine learning project with a dataset containing 1 million records. They are considering whether to use scikit-learn or Spark ML for training their model. Which of the following statements correctly describe scikit-learn's capabilities in this scenario?
- A
Scikit-learn operates on a single node, which may limit its scalability for large datasets.
- B
Scikit-learn can efficiently distribute computations across a cluster, making it suitable for large-scale data processing.
- C
Scikit-learn is well-suited for machine learning tasks on datasets that can fit into the memory of a single machine.
- D
Scikit-learn integrates seamlessly with Spark's distributed processing capabilities for training models on large datasets.
- E
Scikit-learn is often faster than Spark ML for small to medium-sized datasets processed on a single machine.
Show answer and explanation
Correct answers: A, C, E
Explanation
Scikit-learn is a single-node machine learning library designed for datasets that fit into the memory of a single machine. It does not support distributed computations or integrate with Spark's distributed processing capabilities. However, it is often faster and more efficient for small to medium-sized datasets compared to distributed systems like Spark ML. For large-scale datasets that require distributed processing, Spark ML is the more appropriate choice.
- A. Correct.
Correct: Scikit-learn operates on a single node, meaning all computations are done on one machine. This can limit scalability for very large datasets that exceed the memory or processing power of a single machine.
- B. Incorrect.
Incorrect: Scikit-learn does not natively support distributed computations across a cluster. It is designed for single-node operations.
- C. Correct.
Correct: Scikit-learn works well when the dataset can fit into the memory of a single machine, making it suitable for small to medium-sized datasets.
- D. Incorrect.
Incorrect: Scikit-learn does not natively integrate with Spark's distributed processing. Spark ML is specifically built for distributed machine learning on large datasets.
- E. Correct.
Correct: For smaller datasets that can fit into the memory of a single machine, scikit-learn is often faster than Spark ML due to its simpler architecture and lack of distributed overhead.