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You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy requirements. Your team has gathered more data and believes that the model can be further optimized. You are considering various methods to improve the model’s performance, including feature engineering, hyperparameter tuning, and trying different algorithms. However, given the limited time and computational resources, you need to prioritize the most impactful strategies. Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance? (Select two)