Final Exam: Leveraging Generative AI APIs

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Final Exam: Leveraging Generative AI APIs will test your knowledge and application of the topics presented throughout the Leveraging Generative AI APIs journey.


  • Apply best practices when using generative ai
    identify the roots, emerging trends, and advancements of generative ai
    describe how generative ai can have a real-world impact on many industries
    navigate the ethical concerns and implications that generative ai can create, such as bias and misuse
    use generative ais to create text, short stories, ads, or summaries
    identify and describe multiple common generative ai apis and their features
    work with generative ais to generate images by providing textual descriptions
    describe how generative ais can be embedded into business processes or workflows
    parse a response to get results and troubleshoot likely and common errors
    how gpt artificial intelligence (ai) works and outline the capabilities and features of the openai api
    apply parameters, like temperature, to acquire different or better results
    describe how tokens and models affect the pricing of openai and the soft and hard limits for usage of the models
    describe the different api endpoints and their associated models
    create a simple text completion using a model and endpoint
    identify organizational best practices when using openai to handle scaling, latency, and limits
    use the translation api to translate to and from english
    use advanced text generation features to do more complex completions and handle longer input situations
    translate audio speech to and from english with openai
    outline how to use openai prompts to help improve audio and speech translation
    describe the speech recognition features and capabilities in openai
    use openai to generate an image based on a textual description
    outline the process of using openai contrastive language-image pre-training (clip) for object recognition
    describe the process of converting text into speech with openai and adjusting parameters to control the output
    describe how to translate audio speech from one language to another using speech translation in openai
    use fine-tuning to create a custom classification feature
    create a simple customer-facing chatbot that can answer questions
    work with the sentiment analysis api to get feedback about the tone of text
    describe how embeddings can be used for searching, clustering, recommending, and classifying by measuring relatedness
    use embeddings to handle relatedness in terms of clustering and making recommendations
    create similarity measurements and apply classifications to text
  • use fine-tuning to customize a chatbot to handle specific scenarios or questions
    outline what bard is, how it can be used, its history, and its significance to artificial intelligence (ai)
    use bard to generate text content like summaries and descriptions of events
    distinguish the features of bard from other conversation ai offerings like openai's chatgpt
    identify the privacy and ethical concerns of using a generative ai including understanding bias, inaccuracies, and hallucinations
    work with the bard web interface, including the components, sidebar, and chat interface
    recognize the requirements to create a google account to use bard and the acceptable usage of bard
    work with bard to ask questions, get feedback, modify responses, and leverage feedback and drafts
    use the bard web interface with images of animals, places, or objects and have it find an appropriate image
    use the bard web interface to export bard code responses to sites like colab and replit
    use the bard api to create a story, poem, and song lyric
    use the bard api to integrate a tone that will influence the output response from bard
    use the bard api to create an outline for a potential course and create summaries of content
    use the bard api to translate foreign languages passages to english
    identify the languages and capabilities that bard provides for translating languages to and from english
    use the bard api to translate english into other languages and leverage drafts to get a better translation
    list the features and capabilities of bard's analytics
    use bard to analyze data and get suggestions based on the analysis
    control the output formatting of data in responses
    outline the pathways language model (palm) and how it is used to power bard
    identify the features of the palm api and how it can be used
    create a palm api key and verify it works by making a simple request
    recognize the coding languages supported by the palm api and client libraries that can be used to facilitate development
    troubleshoot and diagnose issues that can cause problems when using bard or the api
    add comments to analyze what code is doing and have bard fix bugs
    outline the use of the python client application programming interface (api) library for accessing and using palm
    apply parameters like temperature, candidate count, topp, and topk to affect output
    use optimization techniques to reduce resource usage and improve performance
    outline how safety filters affect responses and can be used to block inappropriate communications
    use the safety filters to check content for explicit material


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