Databricks Certified Data Engineer - Associate
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A data engineering team is exploring the Databricks Lakehouse Platform to enhance their machine learning workflows. They aim to utilize existing open-source libraries for model development and require the ability to scale model training across large datasets efficiently. Considering the need for cost-effectiveness, compliance with data governance policies, and the ability to handle petabytes of data, which of the following statements accurately describes how Databricks Machine Learning meets these requirements and its primary advantage over traditional machine learning platforms? (Choose one correct answer)
Simulated
A
Databricks Machine Learning enforces the use of a proprietary programming language, limiting flexibility but ensuring high performance and scalability through cloud-native technologies.
B
Databricks Machine Learning supports the use of popular open-source tools and languages like Python, R, and MLflow, facilitating a collaborative environment. It excels in performance and scalability for big data processing by leveraging optimized, distributed computing capabilities.
C
Databricks Machine Learning is restricted to Databricks-specific APIs, offering enhanced performance and scalability but at the cost of flexibility and integration with open-source libraries.
D
Databricks Machine Learning is suitable only for small datasets, requiring integration with external platforms for large-scale data processing and model training.
Explanation:
Databricks Machine Learning stands out by allowing data scientists and engineers to work with familiar open-source libraries and languages, such as Python, R, and MLflow, within a unified, collaborative workspace. Its architecture is specifically designed for distributed big data processing, enabling efficient scaling of model training and deployment across massive datasets. This approach not only ensures superior performance and scalability but also adheres to cost-effectiveness and compliance requirements, offering a significant advantage over traditional platforms that may lack such integrated capabilities. Options A, C, and D present inaccuracies regarding the platform's flexibility, scalability, and compatibility with open-source tools, making them incorrect choices.
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