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Answer: Databricks Runtime ML clusters enhance cluster creation speed and guarantee compatibility with the installed library versions.
Databricks Runtime ML clusters provide several benefits tailored for machine learning tasks: - **Efficient cluster setup and library compatibility**: By pre-installing and configuring widely-used machine learning libraries, Databricks Runtime ML eliminates the manual setup, speeding up cluster creation and ensuring your code works seamlessly with the available libraries. Incorrect options clarified: - **Option A**: Databricks Runtime ML clusters are not limited to CPU-only setups; they can be configured for GPU-based clusters as well. - **Option B**: While it's true that Databricks Runtime ML includes popular libraries, this option doesn't highlight the primary benefit of faster cluster creation and compatibility. - **Option C**: Contrary to this statement, Databricks Runtime ML offers an extensive collection of machine learning libraries, not a limited set. In summary, Databricks Runtime ML optimizes the machine learning workflow on Databricks by offering pre-configured clusters with essential libraries, thereby saving time and ensuring a smooth development process.
Author: LeetQuiz Editorial Team
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What advantages do Databricks Runtime ML clusters offer for machine learning workloads?
A
Databricks Runtime ML clusters are exclusively for CPU-enabled ML runtimes.
B
Databricks Runtime ML clusters include a wide range of popular machine learning libraries, such as TensorFlow, PyTorch, scikit-learn, and XGBoost.
C
Databricks Runtime ML clusters support only a minimal selection of machine learning libraries.
D
Databricks Runtime ML clusters enhance cluster creation speed and guarantee compatibility with the installed library versions.
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