Databricks Machine Learning Associate Question 278
Single answerA data scientist is working with a dataset that fits entirely into the memory of a single machine. They want to build a machine learning pipeline for preprocessing and training a model. The project requirements indicate that the solution should be easy to implement and computationally efficient for single-node processing. Which of the following tools would be the most appropriate choice in this scenario?
- A
scikit-learn
- B
Spark ML
- C
TensorFlow
- D
Databricks AutoML
Show answer and explanation
Correct answer: A
Explanation
scikit-learn is a single-node solution designed for machine learning tasks where the dataset fits into memory on a single machine. It provides efficient and intuitive tools for preprocessing and model training. In contrast, Spark ML is built for distributed computing, making it more suitable for large-scale datasets. TensorFlow is focused more on deep learning, and Databricks AutoML is a higher-level tool intended for automating machine learning workflows, not necessarily the most efficient solution for this specific use case.
- A. Correct.
scikit-learn is a single-node solution well-suited for datasets that fit in memory, offering easy-to-use APIs for building machine learning pipelines.
- B. Incorrect.
Spark ML is designed for distributed machine learning and is more appropriate for larger datasets that do not fit in memory or require distributed processing.
- C. Incorrect.
TensorFlow is primarily used for deep learning and is not as simple or efficient for traditional machine learning tasks and preprocessing pipelines on single-node datasets.
- D. Incorrect.
Databricks AutoML is a tool for automating machine learning workflows, which is overkill for this specific scenario where scikit-learn can handle the task efficiently.