manufacturing reports. C. Use OCI Vision’s custom model feature to train the AI with labeled defect images for accurate defect detection. D. Use OCI Vision’s built-in machine learning model to detect defects by analyzing real-time sensor data from the manufacturing line. Explanation: The best approach to using OCI Vision for the quality control process in manufacturing is to use OCI Vision’s custom model feature to train the AI with labeled defect images for accurate defect detection. This allows the system to learn from specific examples of defects, improving its accuracy in identifying issues in products. Which of the following are best practices when fine-tuning a pre-trained Large Language Model (LLM) in Oracle Cloud Infrastructure to ensure optimal performance and avoid overfitting? (Select two)
B. Supervised learning requires labeled data, whereas unsupervised learning requires structured data. C. Supervised learning and unsupervised learning both require labeled data but use different algorithms. D. In supervised learning, the model learns from labeled data to make predictions, whereas in unsupervised learning, the model finds hidden patterns in unlabeled data. Explanation: The difference between supervised and unsupervised learning in machine learning is best described by C: In supervised learning, the model learns from labeled data to make predictions, whereas in unsupervised learning, the model finds hidden patterns in unlabeled data. This highlights the fundamental distinction in the type of data used and the objectives of each learning approach. Which of the following statements best describes the role of prompt engineering in the context of Large Language Models (LLMs) in Oracle Cloud Infrastructure AI Foundations?
Explanation: Supervised learning in the context of machine learning is defined as a learning process in which the model is trained on labeled data, where each input is paired with the correct output. This method allows the model to learn from examples and make predictions based on new, unseen data. You are a data scientist at a company building a machine learning model for predictive maintenance using Oracle Cloud Infrastructure (OCI) AI services. Your dataset is highly imbalanced, with 95% of the records indicating no failure and only 5% indicating failure. You need to train a model using OCI AI Foundations while ensuring that the imbalance in the dataset does not negatively impact the model’s ability to predict failures. What would be the most effective approach in OCI AI Foundations to handle the imbalance in your dataset and improve model performance?
language descriptions for products in an e-commerce catalog are OCI Generative AI and OCI AI Language. These services are designed for tasks involving text generation and natural language processing. Which feature of Oracle Cloud Infrastructure's (OCI) AI services is most beneficial for developers who want to quickly integrate AI capabilities into their applications without having to build models from scratch?
Which of the following best describes the key advantage of deep learning over traditional machine learning algorithms?
they scale. Explanation: A key factor that enables transformers to scale effectively in large language models (LLMs) compared to traditional neural networks is that transformers can be parallelized, reducing the training time even as the model size increases. This parallelization capability allows for more efficient training on large datasets and complex models. When utilizing the Oracle Cloud Infrastructure (OCI) Data Science service, which of the following is a key feature that enhances collaboration among data science teams?
