Databricks Machine Learning Associate Question 409
Select 3A data science team is working on a large-scale machine learning project in Databricks and is considering using ML runtimes. Which of the following are advantages of using ML runtimes in Databricks?
- A
Pre-installed and optimized libraries for machine learning tasks
- B
Automatic model selection and hyperparameter tuning
- C
Seamless integration with distributed computing frameworks like Apache Spark
- D
Support for custom machine learning models using user-defined libraries
- E
Built-in GPU acceleration for deep learning workloads
Show answer and explanation
Correct answers: A, C, E
Explanation
ML runtimes in Databricks offer several advantages, including pre-installed libraries, seamless integration with distributed frameworks, and GPU acceleration for deep learning. These features simplify the machine learning workflow while maintaining scalability and efficiency. However, they do not automatically handle tasks like model selection or hyperparameter tuning, and user-defined libraries require additional setup.
- A. Correct.
ML runtimes in Databricks come with pre-installed and optimized libraries, such as scikit-learn, TensorFlow, PyTorch, and others. This eliminates the need for manual setup and ensures compatibility with the platform.
- B. Incorrect.
Databricks ML runtimes do not automatically handle model selection or hyperparameter tuning. These tasks require separate tools or frameworks, such as Hyperopt, which can be manually integrated.
- C. Correct.
ML runtimes seamlessly support distributed computing using Apache Spark, which is one of the core strengths of Databricks. This enables scalable training and data preprocessing.
- D. Incorrect.
While custom models can be used, ML runtimes are specifically designed to leverage pre-installed libraries and frameworks. User-defined libraries require additional setup, which is not a direct advantage of ML runtimes.
- E. Correct.
ML runtimes provide built-in support for GPU acceleration, making it easier to train deep learning models efficiently without complex configuration.