AWS Certified Cloud Practitioner
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A machine learning team wants to integrate LLM capabilities into an existing app quickly. They want—no model training, no GPU resource management, multiple foundation models available, optional fine-tuning in the future. Which approach should they use?
Real Exam
Community
RRitesh
A
Deploy their own models in ECS with autoscaling
B
Use Amazon SageMaker JumpStart for deployment
C
Use Amazon Bedrock with FM access and fine-tuning support
D
Use Amazon Kendra for text generation
Explanation:
Amazon Bedrock is the correct choice because:
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No model training required - Bedrock provides access to pre-trained foundation models from leading AI companies (Anthropic, AI21 Labs, Cohere, Meta, etc.)
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No GPU resource management - AWS manages all the underlying infrastructure, including GPU resources
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Multiple foundation models available - Bedrock offers a choice of various foundation models through a single API
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Optional fine-tuning support - Bedrock supports fine-tuning capabilities for customization when needed
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Quick integration - Bedrock provides APIs that can be easily integrated into existing applications
Why other options are incorrect:
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A (ECS with autoscaling): Requires managing your own models and GPU resources, which contradicts the requirements
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B (SageMaker JumpStart): While it provides pre-built models, it still requires more setup and management than Bedrock
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D (Amazon Kendra): Kendra is a search service, not a text generation service, and doesn't provide LLM capabilities
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