AWS Certified AI Practitioner AIF-C01 Practice Question
To limit harmful outputs when a generative model running on Amazon Bedrock summarizes customer chats, which action most directly improves the system's safety and controllability?
Create Guardrails for Amazon Bedrock that deny specific topics and filter sensitive terms.
Enable automatic compression of the training data stored in Amazon S3.
Deploy the inference endpoint on Spot Instances to lower compute costs.
Increase the embedding vector size used by the model to boost search accuracy.
Guardrails for Amazon Bedrock let builders define denied topics, blocklists, and other content rules that are enforced on model inputs and outputs. These rules help prevent the system from producing disallowed, unsafe, or out-of-scope responses, giving operators a controllable way to reduce risk. Compressing data, choosing Spot Instances, or enlarging embedding dimensions affect cost or performance but do not actively constrain model behavior, so they do not address safety or controllability.
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What are guardrails in Amazon Bedrock and how do they improve safety?
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How do guardrails differ from model training updates in controlling harmful outputs?
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What kinds of harmful output can Amazon Bedrock guardrails help prevent?
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What are Amazon Bedrock Guardrails?
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How do denied topics and sensitive term filters improve model safety?
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What is the difference between Guardrails and training data preprocessing?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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