Bedrock & Knowledge Base — AWS GenAI Dev Practice Question
A representative AWS Generative AI Developer (AWS GenAI Dev) exam question on Bedrock & Knowledge Base. Work through it below, then read why each option is right or wrong.
This question is part of the free AWS Generative AI Developer - Professional practice test.
Short answer
The correct answer is A. Create a Bedrock Knowledge Base with an OpenSearch Serverless vector store over the S3 documentation using Claude 3 Haiku for generation, and configure Bedrock Guardrails with a denied topics policy to block competitor mentions.
A Knowledge Base backed by OpenSearch Serverless performs semantic retrieval over the S3 docs, and Haiku gives the lowest latency and cost among Bedrock models, fitting the budget. Guardrails' denied topics policy is the managed way to reliably suppress competitor mentions. Fine-tuning with provisioned throughput is costly overkill for grounding in existing docs. Pasting all documentation into the prompt blows past context limits and inflates per-request cost. Relying only on a system-prompt instruction to suppress competitor mentions is unreliable compared to a dedicated guardrail, and a relational lookup skips semantic search entirely.
The Question
A developer is building a customer support chatbot on Amazon Bedrock. It must handle multi-turn conversations, ground answers in product documentation stored in S3, never mention competitor products, minimize latency, and keep inference cost under $500/month for 10,000 conversations/day. Which architecture best satisfies all of these requirements?
Why A is correct
A Knowledge Base backed by OpenSearch Serverless performs semantic retrieval over the S3 docs, and Haiku gives the lowest latency and cost among Bedrock models, fitting the budget. Guardrails' denied topics policy is the managed way to reliably suppress competitor mentions. Fine-tuning with provisioned throughput is costly overkill for grounding in existing docs. Pasting all documentation into the prompt blows past context limits and inflates per-request cost. Relying only on a system-prompt instruction to suppress competitor mentions is unreliable compared to a dedicated guardrail, and a relational lookup skips semantic search entirely.
Why the other options are wrong
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Key idea: Bedrock & Knowledge Base
A Knowledge Base backed by OpenSearch Serverless performs semantic retrieval over the S3 docs, and Haiku gives the lowest latency and cost among Bedrock models, fitting the budget. Guardrails' denied topics policy is the managed way to reliably suppress competitor mentions. Fine-tuning with provisioned throughput is costly overkill for grounding in existing docs. Pasting all documentation into the prompt blows past context limits and inflates per-request cost. Relying only on a system-prompt instruction to suppress competitor mentions is unreliable compared to a dedicated guardrail, and a relational lookup skips semantic search entirely. On the AWS GenAI Dev exam, questions in the "Foundation Model Integration, Data Management, and Compliance" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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