AIF-C01 Question 86
Select 3You are tasked with developing a generative AI application that creates realistic images from textual descriptions. Which AWS services and technologies would you use to build and deploy this application efficiently?
- A
Amazon SageMaker JumpStart for accessing pre-trained generative AI models
- B
Amazon Rekognition for generating images from text
- C
AWS Lambda for scalable, serverless inference of the generative AI model
- D
Amazon Elastic Kubernetes Service (EKS) for managing containerized training and inference workloads
- E
AWS CodePipeline for continuous integration and deployment (CI/CD) of your generative AI application
Show answer and explanation
Correct answers: A, D, E
Explanation
Building generative AI applications on AWS involves leveraging services tailored for machine learning tasks, such as SageMaker JumpStart for pre-trained models, EKS for containerized workloads, and CodePipeline for deploying updates efficiently. These services together provide a robust infrastructure for training, deploying, and maintaining generative AI models.
- A. Correct.
Amazon SageMaker JumpStart provides pre-trained generative AI models and solutions that can be quickly deployed and fine-tuned, making it a key service for developing generative AI applications.
- B. Incorrect.
Amazon Rekognition is used for image and video analysis tasks, such as object detection and facial recognition, but it is not capable of generating images from text.
- C. Incorrect.
AWS Lambda is a serverless compute service but is not designed for the heavy computational requirements of generative AI model inference.
- D. Correct.
Amazon Elastic Kubernetes Service (EKS) is well-suited for managing containerized workloads, including scalable training and inference of generative AI models.
- E. Correct.
AWS CodePipeline helps automate CI/CD workflows, which is essential for deploying updates to your generative AI application efficiently.