Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Comments
Loading comments...
Ultimate access to all questions.
You are working on a project that requires training a deep learning model with a large dataset. The dataset is too large to fit in memory, and you need to use distributed computing to train the model. Explain how you would use Apache Spark to train the model efficiently and handle the large dataset.
A
To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's data parallelism to train the model on each partition in parallel. Finally, you would aggregate the results from each partition to update the model parameters.
B
To train the deep learning model efficiently using Apache Spark, you would first load the entire large dataset into memory. Then, you would use Spark's data parallelism to train the model on the dataset in parallel. Finally, you would update the model parameters based on the results from each partition.
C
To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's model parallelism to train the model on each partition sequentially. Finally, you would aggregate the results from each partition to update the model parameters.
D
To train the deep learning model efficiently using Apache Spark, you would first partition the large dataset into smaller chunks and distribute them across the Spark cluster. Then, you would use Spark's data parallelism to train the model on each partition sequentially. Finally, you would update the model parameters based on the results from each partition.