AIF-C01 Fundamentals of Generative AI Practice Question
A media company runs batch inference jobs to generate captions for thousands of images weekly using a foundation model on Amazon Bedrock. They want to minimize costs while maintaining predictable throughput. Which pricing option should they choose?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Batch Transform or EC2 Spot instances with Bedrock's native pricing options, failing to recognize that Provisioned Throughput is the only Bedrock-specific offering that guarantees predictable throughput and cost efficiency for batch inference workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Provisioned Throughput
Provisioned Throughput on Amazon Bedrock is the correct choice because it reserves a specified level of inference capacity for a foundation model, ensuring predictable throughput for batch workloads while offering a lower per-inference cost compared to on-demand pricing. This option is ideal for the media company's weekly batch inference jobs, as it minimizes costs by committing to a consistent volume of requests without the variability of spot instances or the higher per-request expense of on-demand inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for SageMaker models, not directly applicable to Bedrock.
- ✗
On-demand inference
Why it's wrong here
On-demand is suitable for low or variable traffic; for high batch volume, Provisioned Throughput is more cost-effective.
- ✓
Provisioned Throughput
Why this is correct
Reserves capacity for a model, providing consistent performance and lower per-token cost for large batches.
- ✗
Spot instances (EC2 Spot)
Why it's wrong here
Bedrock does not offer spot pricing; it uses a serverless model.
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