### Q1: What are the three critical phases of the model training lifecycle in AI?  
### Q2: How can fine-tuning hyperparameters during the validation phase improve an AI model's performance?  
### Q3: Why is it important to test an AI model on a completely new dataset?  

### Q4: When might it be more beneficial to focus on precision rather than accuracy in evaluating an AI model?  
### Q5: In what cases should the F1 score be prioritized over accuracy when assessing model performance?  
### Q6: How does the Mean Squared Error (MSE) metric reflect the performance of a regression model?  

### Q7: How can effective communication about model evaluation metrics improve team collaboration in AI projects?  
### Q8: What are some strategies to convey the importance of the validation phase to stakeholders who may not have a technical background?  
### Q9: How can you articulate the significance of the ROC-AUC score in a presentation to non-technical audiences?