### Q1: What are the three popular AI engineering tools mentioned in the content, and which one is known for its dynamic computation graph?  
### Q2: How does using Pandas facilitate data manipulation and analysis in AI engineering tasks?  
### Q3: In what scenarios would using Docker and Kubernetes be beneficial for model deployment and monitoring?  
### Q4: Which approach to AI engineering would you choose for rapid prototyping and why?  
### Q5: How can the choice of AI tools impact the deployment of machine learning models in a fintech application?  
### Q6: What are some advantages of using cloud platforms like AWS for building and deploying AI models?  
### Q7: How does GitHub enhance team collaboration in AI engineering projects?  
### Q8: What are the potential risks of not using version control in an AI engineering environment?  
### Q9: Why is it essential to monitor AI models post-deployment, and what tools can assist in this process?