AWS Certified AI Practitioner · Free Practice Question Medium
Question 19
A media analytics company utilizes Amazon Bedrock to run inferences with its generative AI models to analyze large volumes of user-generated content and provide insights to its clients. The company frequently processes numerous inference requests and is looking for a way to minimize the costs associated with running these inferences while still maintaining the required level of service. Given that the company can tolerate some delays in receiving responses, it seeks a cost-effective inference method that optimizes resource usage without sacrificing too much on turnaround time.
Which inference approach would be the most suitable for the company to use in order to reduce its overall inference costs?
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A
The company should use batch inference, thereby allowing it to run multiple inference requests in a single batch
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B
The company should use serverless inference, which automatically scales resources based on traffic
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C
The company should use on-demand inference, which allows the company to pay only for the resources consumed during each inference
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D
The company should use real-time inference, which is designed for low-latency responses and continuous, immediate processing
Reveal correct answer
Correct answer: A
Explanation
Correct option:
The company should use batch inference, thereby allowing it to run multiple inference requests in a single batch
You can use batch inference to run multiple inference requests asynchronously, and improve the performance of model inference on large datasets. Amazon Bedrock offers select foundation models (FMs) from leading AI providers like Anthropic, Meta, Mistral AI, and Amazon for batch inference at 50% of on-demand inference pricing.
Batch inference is the most cost-effective choice when reducing inference costs on Amazon Bedrock. By processing large numbers of data points in a single batch, the company can lower the cost per inference as the model handles multiple requests simultaneously. This approach is ideal when there is no need for immediate responses, allowing for more efficient use of resources and minimizing computational expenses.
Incorrect options:
The company should use real-time inference, which is designed for low-latency responses and continuous, immediate processing
The company should use serverless inference, which automatically scales resources based on traffic
Real-time inference and serverless inference options apply to Amazon SageMaker, rather than Amazon Bedrock. Amazon Bedrock only offers on-demand or batch inference options. Therefore, both these options are incorrect.
The company should use on-demand inference, which allows the company to pay only for the resources consumed during each inference - On-demand inference offers flexibility by charging only for the resources used during each inference, making it suitable for unpredictable or variable usage patterns. However, it is generally more costly when used frequently or over long periods because it does not benefit from cost savings associated with bulk processing. For a company looking to reduce costs, on-demand inference may not be the most economical option.
References:
https://aws.amazon.com/about-aws/whats-new/2024/08/amazon-bedrock-fms-batch-inference-50-price/
https://docs.aws.amazon.com/bedrock/latest/userguide/inference.html
https://aws.amazon.com/about-aws/whats-new/2024/08/amazon-bedrock-fms-batch-inference-50-price/
https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html
Discussion
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