Final Exam: Demystifying Generative AI

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Final Exam: Demystifying Generative AI will test your knowledge and application of the topics presented throughout the Demystifying Generative AI track.


  • Define generative ai and its key components
    differentiate between generative ai and other ai methods, showcasing their unique features and use cases
    identify the fundamental principles and theories that drive generative ai
    outline the various tools, technologies, platforms, and communities that comprise the generative ai ecosystem
    describe generative models like gans (generative adversarial networks) and vaes (variational autoencoders), including their structure and function
    outline the potential biases, ethical considerations, and societal impact when designing and using generative ai
    identify popular types of generative models
    outline how generative models are trained using images and practical examples
    outline bayes theorem examples of prior probability, posterior probability, likelihood, and evidence
    recognize the differences between generative and discriminative modeling
    provide an overview of generative adversarial networks (gans) and their groundbreaking applications
    define large language models and outline their significance in the world of artificial intelligence (ai)
    outline the key components and general architecture that make up typical llms
  • contrast the pre-training methods used in training llms
    use a pre-trained language model for text generation and recognize its constraints
    analyze the architectural components of large language models (llms) and their role in generative artificial intelligence (ai)
    implement techniques to fine-tune large language models for specific generative tasks
    analyze the challenges and limitations of large language models in generative ai applications
    compare and contrast various techniques for controllable and conditioned text generation using large language models
    identify methods for handling bias and promoting fairness in large language models for generative ai
    define the concept of generative artificial intelligence (ai) and its significance in the business landscape
    identify key challenges and opportunities associated with the adoption of generative ai in different industries
    outline the various algorithms and techniques used in generative ai for business applications
    identify the risks and limitations of generative ai implementation and strategies to mitigate them
    develop a strategic roadmap for incorporating generative ai into a business's digital transformation journey


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