Databricks Machine Learning Associate Question 408
Select 3You are working on a machine learning project in Databricks and need to decide whether to use ML runtimes. Which of the following are advantages of using ML runtimes in Databricks?
- A
Pre-installed and optimized libraries for machine learning and deep learning
- B
Built-in support for model deployment across multiple cloud platforms
- C
Automatic performance tuning of hyperparameters without user intervention
- D
Seamless integration with MLflow for experiment tracking and model management
- E
Access to GPU acceleration for faster training of deep learning models
Show answer and explanation
Correct answers: A, D, E
Explanation
ML runtimes in Databricks offer several benefits, including pre-installed and optimized libraries, GPU acceleration for deep learning, and seamless integration with MLflow for tracking and managing experiments. These features help streamline the machine learning workflow, but other tasks like hyperparameter tuning and multi-cloud deployment require additional tools or configurations.
- A. Correct.
Pre-installed and optimized libraries are a key feature of ML runtimes, simplifying the setup process and ensuring compatibility with major ML tools.
- B. Incorrect.
ML runtimes do not natively handle model deployment across multiple cloud platforms. This is typically managed through other tools or custom configurations.
- C. Incorrect.
While ML runtimes offer optimized environments, they do not automatically tune hyperparameters; this requires separate tools or manual implementation.
- D. Correct.
ML runtimes are tightly integrated with MLflow, making it easier to track experiments, manage models, and streamline the ML lifecycle.
- E. Correct.
ML runtimes provide access to GPU acceleration, which significantly speeds up training for computationally intensive deep learning models.