The correct answer is C because Amazon Bedrock with On-Demand Throughput is designed for variable or unpredictable workloads. It allows you to pay only for what you use, which is ideal for early-stage or pilot-phase generative AI applications.
From AWS documentation:
"On-Demand Throughput in Amazon Bedrock provides flexibility and cost-efficiency for unpredictable or low-volume generative AI applications. You are charged based on the number of input/output tokens processed, with no need to provision dedicated capacity."
Explanation of other options:
A. Using GPU-powered EC2 instances requires managing infrastructure and is generally more expensive, especially for inconsistent usage.
B. Provisioned Throughput is best suited for predictable, high-volume production workloads where guaranteed throughput is needed.
D. SageMaker JumpStart is for training and deploying ML models, not for efficient inference of foundation models.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Pricing and Throughput Models
AWS Generative AI Best Practices – Cost Optimization
AWS ML Specialty Guide – Bedrock Deployment Models
