E: The vanishing gradient problem commonly occurs in RNNs, making it difficult to learn long -term dependencies. This issue can hinder the model's ability to remember information from earlier in the sequence, affecting performance on tasks requiring long -term memory. A healthcare company wants to develop a solution that can automatically extract relevant medical entities, such as symptoms, medications, and diagnoses, from large volumes of unstructured medical records. They plan to use Oracle Cloud Infrastructure's (OCI) Language service to achieve this goal. As the AI architect, you are tasked with designing a solution. You need to choose the most appropriate feature within OCI AI services to extract these entities effectively. Which OCI AI service feature should you recommend to solve this problem?
building and deploying machine learning models on OCI. Which of the following services within the Oracle Cloud Infrastructure (OCI) AI Portfolio is best suited for automating language understanding tasks, such as extracting sentiment or identifying named entities from text data?
number of computations required during inference. B. Instruction tuning increases the model’s capacity by adding additional layers of neural networks, making it more powerful in handling large datasets. C. Instruction tuning trains the model to better follow explicit instructions in the input prompt, leading to more accurate and relevant responses. D. Instruction tuning allows the model to automatically detect and fix errors in the input prompt, improving the overall quality of the output. Explanation: Instruction tuning trains the model to better follow explicit instructions in the input prompt, leading to more accurate and relevant responses. This process helps the model adapt to user expectations and specific tasks, improving its overall performance. You are tasked with building an image classification model to detect different types of skin diseases from high-resolution images of skin lesions. The dataset is large, containing thousands of labeled images. Your goal is to design a deep learning model that captures spatial hierarchies of patterns within the images, such as edges, textures, and more complex features. Given the nature of the task, which architecture would be most suitable for this scenario?
OCI Generative AI Services allow users to fine-tune pre-built generative AI models using their own datasets and business requirements. This capability enables customization for specific use cases, enhancing the relevance and accuracy of the generated outputs. In the context of prompt engineering for Large Language Models (LLMs) in Oracle Cloud Infrastructure (OCI), what is the primary role of providing a well -structured prompt?
A logistics company wants to leverage AI in OCI to optimize its operations. The company collects data from various sources, including GPS coordinates, traffic conditions, and delivery times. The company is considering using AI to predict delivery delays and improve routing efficiency. You are tasked with identifying the best approach to handle and analyze this diverse data to create an AI solution. Which of the following AI applications and types of data would be most suitable for this use case?
Explanation: Fine-tuning modifies the underlying architecture of the pre-trained model to fit new tasks by training it on new data. This process allows the model to leverage its existing knowledge while adapting to specific requirements. What is one of the main challenges associated with deploying Generative AI models, like Large Language Models (LLMs), in Oracle Cloud Infrastructure (OCI) for content creation tasks?
