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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q12-Q17):
NEW QUESTION # 12
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
Answer: D
Explanation:
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.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation
NEW QUESTION # 13
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?
Answer: C
Explanation:
In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI
NEW QUESTION # 14
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
Answer: C
NEW QUESTION # 15
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
Answer: C
NEW QUESTION # 16
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?
Answer: D
Explanation:
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.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference
NEW QUESTION # 17
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