AWS Certified AI Practitioner Practice Exam

When using large datasets for training, what is a key advantage of batch processing?

1. Immediate feedback on model performance

2. Cost efficiency with large volumes of data

3. Real-time decision making capabilities

4. Reduction of model complexity

The correct answer is: Cost efficiency with large volumes of data

Batch processing offers significant advantages when working with large datasets, particularly in terms of cost efficiency. When training models on comprehensive datasets, processing the data in large batches can optimize resource utilization, leading to reduced computational costs. Using batch processing allows you to handle extensive volumes of data more effectively because it enables parallel processing and takes advantage of optimized algorithms designed to operate efficiently on bulk data. This thereby lowers the overall costs associated with training, especially in environments where cloud resources are charged based on usage. Immediate feedback on model performance is more closely associated with techniques such as online or incremental learning, which process data in smaller, more frequent iterations. While real-time decision-making capabilities are relevant for streaming data applications, they do not apply directly to the batch processing of large datasets. Similarly, reducing model complexity pertains more to the design of the model itself and the features chosen rather than batch processing techniques. Thus, the primary advantage of batch processing in the context of large datasets lies in its ability to manage resources efficiently and cost-effectively.