A company needs to apply image operations such as transposing and rotating a set of images. Which option is the most operationally efficient way to perform these numeric image transformations?

Implementing an AWS Lambda function is the most operationally efficient choice because it allows for serverless, event-driven execution of image transformations. Lambda functions can be triggered by events (e.g., an image upload to S3), scale automatically, and only incur costs when actively running, making them ideal for on-demand processing tasks like transposing and rotating images. Training a deep neural network is overkill and computationally expensive for simple geometric transformations. Amazon Bedrock LLMs are designed for text-based tasks, not image manipulation. AWS Glue Data Quality is for data cleansing and validation, not image transformations.