Oracle 1z0-1127-24 Oracle Cloud Infrastructure 2024 Generative AI Professional Exam Practice Test

A dedicated RDMA (Remote Direct Memory Access) cluster network is crucial during model fine-tuning and inference because it facilitates high-speed, low-latency communication between GPUs. This capability is essential for scaling up the deployment of multiple fine-tuned models across a GPU cluster.

RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine-tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability.

By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads.

References

Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks

Technical resources on the benefits of RDMA in high-performance computing environments

K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.

References

Research papers on few-shot learning and prompting techniques

Technical documentation on using examples in prompts for large language models

Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:

Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.

Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.

References

Research papers on text embedding comparison metrics

Technical documentation on vector similarity measures

The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.

References

Technical documentation on AI cluster architectures

Research articles on optimizing GPU memory utilization in model inference

In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.

References

Articles on loss functions in machine learning

OCI Generative AI service documentation on model evaluation metrics

Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.

Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.

Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.

References

Research articles on prompting techniques for language models

Documentation on effective use of prompting strategies

The difference between "Top k" and "Top p" in selecting the next token in generative models lies in their selection criteria:

Top k: This method selects the next token from the top k tokens based on their probability scores. It restricts the selection to a fixed number of the most probable tokens, irrespective of their cumulative probability.

Top p: Also known as nucleus sampling, this method selects tokens based on the cumulative probability until it exceeds a certain threshold p. It dynamically adjusts the number of tokens considered, ensuring that the sum of their probabilities meets or exceeds the specified p value. This allows for a more flexible and often more diverse selection compared to Top k.

References

Research articles on sampling techniques in language models

Technical documentation for generative AI models in OCI

The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.

References

Oracle ADS documentation

Guides on deploying AI models using Oracle Data Science services

LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.

References

LangChain documentation on LCEL

Examples and tutorials on using LangChain Expression Language

The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.

References

Research papers on nucleus sampling and token selection methods

OCI Generative AI model documentation

The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:

Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.

High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.

Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.

References

Research articles on the role of temperature in generative models

Technical guides for tuning generative AI models in OCI

Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.

References

Research papers on vector normalization in information retrieval

Technical documentation on hybrid search systems