AWS Certified AI Practitioner AIF-C01 Practice Question
A startup is using an external foundation model API for code suggestions. Developers plan to paste real production database records containing customer names and emails into the prompt for better context. Which primary risk of prompt engineering does this create?
Sensitive customer data could be transmitted to and stored by the external model provider.
Prompt tokens may exceed the model's context window, causing truncation of input.
Longer prompts will increase inference latency and raise API costs.
The model responses might vary unpredictably because temperature is set too high.
Including actual customer records in the prompt can expose personally identifiable information to the model provider or any system that stores prompt logs. This violates data-protection policies and can lead to accidental leakage of sensitive data. While longer prompts may raise latency or cost and high temperature can affect response quality, those issues are performance-related, not security-related. Exceeding the context window would simply truncate the prompt but would not itself leak data; the core concern is that confidential information is being shared outside the organization.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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