AIF-C01 Question 85
Select 3A startup is building a generative AI application to create personalized marketing content for its users. They need a solution that can handle large-scale training and inference while leveraging pre-trained models to reduce development time. Which AWS services and technologies should they consider for this use case?
- A
Amazon SageMaker JumpStart
- B
AWS Inferentia-based EC2 Instances
- C
Amazon Elastic File System (EFS)
- D
Amazon Polly
- E
Amazon Bedrock
Show answer and explanation
Correct answers: A, B, E
Explanation
To build a generative AI application, AWS provides multiple services tailored to the task. Amazon SageMaker JumpStart simplifies the process by offering pre-trained models and solutions. AWS Inferentia-based EC2 Instances enhance inference performance for large-scale workloads. Amazon Bedrock provides easy access to foundation models without requiring infrastructure management. These services collectively address the needs of training, inference, and efficient deployment for generative AI applications.
- A. Correct.
Amazon SageMaker JumpStart provides pre-trained models and end-to-end solutions for machine learning tasks, including generative AI, which can save time and effort in building applications.
- B. Correct.
AWS Inferentia-based EC2 Instances are optimized for running machine learning inference workloads at scale, making them suitable for deploying generative AI models efficiently.
- C. Incorrect.
Amazon Elastic File System (EFS) is a scalable storage service but is not specifically designed for generative AI workloads or model training/inference.
- D. Incorrect.
Amazon Polly is a text-to-speech service and is not directly related to generative AI applications or training large models for personalized content generation.
- E. Correct.
Amazon Bedrock allows developers to access foundation models via an API without managing infrastructure, making it an ideal choice for building generative AI applications.