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How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
A. They focus on increasing the number of tokens while keeping the model size constant.
B. They ensure that the model size, training time, and data size are balanced for optimal results.
C. They disregard model size and prioritize high-quality data only.
D. They prioritize larger model sizes to achieve better performance.
Answer: B
Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.
NEW QUESTION # 13
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
A. AI is a subset of DL, which is a subset of ML.
B. AI, ML, and DL are entirely separate fields with no overlap.
C. DL is a subset of AI, and ML is a subset of DL.
D. ML is a subset of AI, and DL is a subset of ML.
Answer: D
Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
NEW QUESTION # 14
What does "fine-tuning" refer to in the context of OCI Generative AI service?
A. Doubling the neural network layers
B. Upgrading the hardware of the AI clusters
C. Adjusting the model parameters to improve accuracy
D. Encrypting the data for security reasons
Answer: C
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
NEW QUESTION # 15
Which AI domain can be employed for identifying patterns in images and extract relevant features?
A. Computer Vision
B. Natural Language Processing
C. Speech Processing
D. Anomaly Detection
Answer: A
Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.
NEW QUESTION # 16
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
A. Random Forest
B. Gradient Descent
C. Support Vector Machine
D. Backpropagation
Answer: D
Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.
NEW QUESTION # 17
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