Django Master Pro: Advanced Web & Cloud Architect" by Maurizio Mocci
Last updated on août 15, 2024 Engagement: Over 300 ConversationsDjangoMaster Pro specializes in Django web development, emphasizing Python, Django REST, and TDD. Expert in Docker, Kubernetes, and CI/CD, it offers robust API development, front-end skills, and cloud technologies for scalable web solutions. https://www.linkedin.com/in/maurizio-mocci-47b233b5/
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Prompt Starters
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We're experiencing slow query times on our user table as it grows. How would you approach diagnosing and optimizing this issue in Django?
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A change in our business logic requires modifications to an API endpoint that is already in use. How would you handle versioning for this endpoint in Django REST framework?
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If you're tasked with breaking a monolithic Django app into microservices, what factors would you consider for the separation of concerns?
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How would you containerize an existing Django application using Docker for development and production?
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Describe how you would configure a Django app deployment on Kubernetes to automatically scale during peak traffic times.
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Can a Django application be adapted to a serverless architecture, and what are the benefits and drawbacks of doing so?
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A security vulnerability has been announced in a Django package you're using. Describe your process for responding to this issue.
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What approach would you take to implement a real-time chat feature in a Django application using WebSockets?
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Explain how you would set up a CI/CD pipeline for a Django project. Which tools would you use and why?
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You're tasked with managing AWS infrastructure for a Django app using Infrastructure as Code. Which tool would you choose and what would be your first steps?
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How would you integrate a non-relational database into a Django project that traditionally uses PostgreSQL?
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Our Django application is facing performance bottlenecks with its REST API under heavy load. How would you identify the bottlenecks and what steps would you take to alleviate them?"
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Python: The GPT can write and run Python code in a stateful Jupyter notebook environment. It supports file uploads, performs advanced data analysis, handles image conversions, and can execute Python scripts with a timeout for long-running operations.
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