MLA-C02 practice question: Guardrails and responsible AI (1 of 3)
Operating, Monitoring, and Securing ML and AI Solutions A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A regional bank deploys a customer chatbot powered by a Bedrock model. The compliance team has said that the chatbot must not give investment advice, and that it must not reveal a customer's account numbers, Social Security numbers or home addresses in its responses.
The protections should apply to the model's inputs and outputs in one place, so that they do not depend on each application team writing its own filters.
Which solution meets these requirements?
Choose one answer.
AA. Add a line to the system prompt that tells the model never to give investment advice or reveal account numbers or other personal data.
BB. Have each application team write a regular expression filter in its own code to remove account numbers from the responses.
CC. Restrict the chatbot to a smaller model, since smaller models do not discuss investments or personal data.
DD. Create a Bedrock Guardrail with a denied investment-advice topic and a sensitive information filter for account numbers and addresses, attached to the model calls.
Show the answer and explanation
Answer: D
A. Wrong: A prompt instruction depends on the model following it and is not enforced on inputs or outputs. The stem asks for controls that do not depend on each application.
B. Wrong: Filters written in each application depend on every team, and they do not cover topic restrictions such as investment advice. The stem asks for one central control.
C. Wrong: Model size does not determine which topics a model discusses or whether it reveals personal data. The option does not enforce the rules.
Bedrock Guardrails can deny a topic, such as investment advice, and filter or mask sensitive information such as account numbers and addresses in both inputs and outputs. Attaching the guardrail to the model calls applies the policy centrally, while prompt rules, per-team filters and smaller models do not enforce it.
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