Free Databricks-Generative-AI-Engineer-Associate Actual Exam Questions - Question 10 Discussion
personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for
malicious user inputs?
A imo, since just limiting time (B) or boosting compute (D) doesn't stop bad inputs. Also, letting the convo continue with a warning (C) still risks harmful content slipping through.
It’s A for sure. B and D don’t actually prevent anything harmful; they just try to limit interaction or speed. C lets the bad input slide by, which could lead to inappropriate outputs. Having a solid filter that blocks harmful inputs outright is the best way to keep the app safe and maintain control over the content generated.
A. Having a dedicated filter that outright blocks harmful inputs feels like the safest bet here. Options B and D don’t actually stop bad content, and C risks enabling toxicity by continuing the chat.
C imo, because just warning the user about malicious input might discourage bad behavior without completely shutting down the interaction, keeping some user freedom without ignoring the risk.
B and D don’t really address malicious input itself—limiting time or adding compute won’t stop harmful content from getting through. C seems risky since it still engages with bad input. Isn’t A the only one directly blocking harmful inputs?
Maybe A, since limiting interaction time (B) doesn’t really stop bad inputs.
It’s A because just limiting interaction time (B) or boosting compute (D) won't stop malicious prompts. Letting the LLM continue after a warning (C) seems risky, so blocking harmful inputs outright feels safest.
Option A, since B and D don’t really address input safety risks.