OCI AI Foundations Associate

What is the advantage of using Oracle Cloud Infrastructure Supercluster for AI workloads?. It is ideal for tasks such as text-to-speech conversion. It offers seamless integration with social media platforms. It delivers exceptional performance and scalability for complex AI tasks. It provides a cost-effective solution for simple AI tasks.

You are the lead developer of a Deep Learning research team, and you are tasked with improving the training speed of your deep neural networks. To accelerate the training process, you decide to leverage specialized hardware. Which hardware component is commonly used in Deep Learning to accelerate model training?. Graphics Processing Unit (GPU). Solid-State Drive (SSD). Central Processing Unit (CPU). Random Access Memory (RAM).

Which NVIDIA GPU is offered by Oracle Cloud Infrastructure?. T4. K80. A100. P200.

What is the primary purpose of reinforcement learning?. Finding relationships within data sets. Learning from outcomes to make decisions. Identifying patterns in data. Making predictions from labeled data.

Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?. Active learning. Reinforcement learning. Unsupervised learning. Supervised learning.

What is the difference between classification and regression in Supervised Machine Learning?. Classification and regression both assign data points to categories. Classification and regression both predict continuous values. Classification predicts continuous values, whereas regression assigns data points to categories. Classification assigns data points to categories, whereas regression predicts continuous values.

Which type of machine learning is used for already labeled data sets?. Unsupervised learning. Active learning. Supervised learning. Reinforcement learning.

You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories – Low Risk, Moderate Risk, and High Risk – based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?. Regression. Multi-Class Classification. Binary Classification. Clustering.

As an IT manager for your company, you are responsible for migrating your company's image and video analysis workloads to Oracle Cloud Infrastructure (OCI). Your team is particularly interested in a cloud service that offers advanced computer vision capabilities, including custom model training. Which OCI service would you consider for this purpose?. OCI Document Understanding. OCI Vision. OCI Speech. OCI Language.

What is the primary function of Oracle Cloud Infrastructure Speech service?. Recognizing objects in images. Transcribing spoken language into written text. Converting text into images. Analyzing sentiment in text.

Which capability is supported by the Oracle Cloud Infrastructure Vision service?. Detecting and preventing fraud in financial transactions. Detecting and classifying objects in images. Analyzing historical data for unusual patterns. Generating realistic images from text.

How does Oracle Cloud Infrastructure Anomaly Detection service contribute to fraud detection?. By identifying abnormal patterns in data. By generating spoken language from text. By analyzing text sentiment. By transcribing spoken language.

How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?. By generating lifelike speech from text. By automating data extraction from documents. By analyzing text sentiment. By transcribing spoken language.

Which capability is supported by Oracle Cloud Infrastructure Language service?. Translating speech into text. Detecting objects and scenes in images. Converting text into images. Analyzing text to extract structured information like sentiment or entities.

Which AI task involves audio generation from text?. Text to speech. Speech recognition. Text summarization. Audio recording.

Which AI domain is associated with tasks such as recognizing faces in images and classifying objects?. Natural Language Processing. Anomaly Detection. Speech Processing. Computer Vision.

Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?. Computer Vision. Speech Processing. Anomaly Detection. Natural Language Processing.

How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?. They focus on increasing the number of tokens while keeping the model size constant. They ensure that the model size, training time, and data size are balanced for optimal results. They prioritize larger model sizes to achieve better performance. They disregard model size and prioritize high-quality data only.

What role do tokens play in Large Language Models (LLMs)?. They determine the size of the model's memory. They represent the numerical values of model parameters. They are used to define the architecture of the model's neural network. They are individual units into which a piece of text is divided during processing by the model.

What is the purpose of Attention Mechanism in Transformer architecture?. Convert tokens into numerical forms (vectors) that the model can understand. Break down a sentence into smaller pieces called tokens. Apply a specific function to each word individually. Weigh the importance of different words within a sequence and understand the context.

What is the difference between Large Language Models (LLMs) and traditional machine learning models?. LLMs focus on image recognition tasks. LLMs have a limited number of parameters compared to other models. LLMs require labeled output for training. LLMs are specifically designed for natural language processing and understanding.

Which is an application of Generative Adversarial Networks (GANs) in the context of Generative AI?. Classification of data points into categories. Prediction of continuous values from input data. Generation of labeled outputs for training. Creation of realistic images that resemble training data.

How is Generative AI different from other AI approaches?. Generative AI understands underlying data and creates new examples. Generative AI focuses on decision-making and optimization. Generative AI generates labeled outputs for training. Generative AI is used exclusively for text-based applications.

What is the purpose of fine-tuning Large Language Models?. To increase the complexity of the model architecture. To prevent the model from overfitting. To reduce the number of parameters in the model. To specialize the model's capabilities for specific tasks.

What is "in-context learning" in the realm of Large Language Models (LLMs)?. Training a model on a diverse range of tasks. Teaching a model through zero-shot learning. Modifying the behavior of a pretrained LLM permanently. Providing a few examples of a target task via the input prompt.

How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?. Customizes the model architecture. Trains a model from scratch. Guides the model's response using predefined prompts. Involves post-processing model outputs and optimizing hyperparameters.

Which Deep Learning model is well-suited for processing sequential data, such as sentences?. Generative Adversarial Network (GAN). Variational Autoencoder (VAE). Recurrent Neural Network (RNN). Convolutional Neural Network (CNN).

What is the primary purpose of Convolutional Neural Networks (CNNs)?. Creating music compositions. Processing sequential data. Detecting patterns in images. Generating images.

What is the primary goal of machine learning?. Creating algorithms to solve complex problems. Explicitly programming computers. Improving computer hardware. Enabling computers to learn and improve from experience.

In machine learning, what does the term "model training" mean?. Analyzing the accuracy of a trained model. Performing data analysis on collected and labeled data. Writing code for the entire program. Establishing a relationship between input features and output.