Google Professional Machine Learning Engineer
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You are a Machine Learning Engineer at a tech company that has recently deployed a model to predict loan approvals. After three months of deployment, an audit reveals that the model's performance is significantly worse for applicants from certain demographic subgroups, raising concerns about biased outcomes. The investigation suggests that the training data was imbalanced, with underrepresented groups not adequately represented. Due to privacy regulations, collecting additional data is not an option. The company is now looking for strategies to mitigate this bias without violating compliance constraints. Which two strategies would you recommend to best address this issue? (Choose two.)
You are a Machine Learning Engineer at a tech company that has recently deployed a model to predict loan approvals. After three months of deployment, an audit reveals that the model's performance is significantly worse for applicants from certain demographic subgroups, raising concerns about biased outcomes. The investigation suggests that the training data was imbalanced, with underrepresented groups not adequately represented. Due to privacy regulations, collecting additional data is not an option. The company is now looking for strategies to mitigate this bias without violating compliance constraints. Which two strategies would you recommend to best address this issue? (Choose two.)
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