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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Encoder-decoder
B. Generator
C. Ranker
D. Retriever

Answer: C

Explanation:
In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.
Reference
Research papers on RAG (Retrieval-Augmented Generation)
Technical documentation on the architecture of RAG models

NEW QUESTION # 45
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Temperature has no effect on probability distribution; it only changes the speed of decoding.
B. Decreasing the temperature broadens the distribution, making less likely words more probable.
C. Increasing the temperature removes the impact of the most likely word.
D. Increasing the temperature flattens the distribution, allowing for more varied word choices.

Answer: D

Explanation:
Temperature is a parameter in LLM decoding algorithms that controls randomness in text generation.
Effects of Temperature on Text Generation:
Higher Temperature (>1.0):
Flattens the probability distribution, making lower-probability words more likely.
Increases randomness, resulting in more creative and diverse outputs.
Lower Temperature (<1.0):
Sharpening effect, making high-probability words more dominant.
Produces more predictable and deterministic responses.
Why Other Options Are Incorrect:
(B) is incorrect because temperature does not remove the impact of likely words; it reduces or increases randomness.
(C) is incorrect because temperature affects probability, not speed.
(D) is incorrect because decreasing the temperature narrows the distribution, making text more deterministic.
🔹 Oracle Generative AI Reference:
Oracle AI models allow dynamic temperature control to balance coherence and creativity in text generation.

NEW QUESTION # 46
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Summarization models
B. Generation models
C. Embedding models
D. Translation models

Answer: D

NEW QUESTION # 47
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When the LLM does not perform well on a task and the data for prompt engineering is too large
B. When you want to optimize the model without any instructions
C. When the LLM already understands the topics necessary for text generation
D. When the LLM requires access to the latest data for generating outputs

Answer: A

Explanation:
Fine-tuning is a technique used to customize an existing Large Language Model (LLM) by training it on domain-specific or task-specific data. Fine-tuning is necessary when:
The LLM's General Knowledge is Insufficient - If the model struggles with a specialized domain (e.g., medical, legal, finance), fine-tuning helps by exposing it to relevant domain-specific data.
Prompt Engineering is Ineffective Due to Large Data Requirements - When a task requires significant custom instructions or examples, fine-tuning is a better approach than prompt engineering, which may have length and complexity limitations.
Improved Accuracy is Required - Fine-tuning helps tailor the model to perform specific tasks more accurately, as it learns from additional training data.
Adapting to a Changing Knowledge Base - Fine-tuning can help update the model with recent trends or company-specific data that were not available during its initial training.
🔹 Oracle Generative AI Reference:
Oracle supports LLM fine-tuning within its AI ecosystem, allowing enterprises to optimize pre-trained AI models for industry-specific applications.

NEW QUESTION # 48
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It leads to higher latency in model inference.
B. It enables the deployment of multiple fine-tuned models.
C. It increases G PU memory requirements for model deployment.
D. It limits the number of fine-tuned model deployable on the same GPU cluster.

Answer: B

Explanation:
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.
Reference
Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high-performance computing environments

NEW QUESTION # 49
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