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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

  • A. Assigns a penalty to tokens that have already appeared in the preceding text
  • B. Specifies a string that tells the model to stop generating more content
  • C. Determines the maximum number of tokens the model can generate per response
  • D. Controls the randomness of the model's output, affecting its creativity

Answer: D


NEW QUESTION # 29
Which statement is NOT true about StreamlitChatMessageHistory?

  • A. StreamlitChatMessageHistory can be used in any type of LLM application.
  • B. A given StreamlitChatMessageHistory will not be shared across user sessions.
  • C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
  • D. A given StreamlitChatMessageHistory will NOT be persisted.

Answer: A

Explanation:
StreamlitChatMessageHistory is a chat message storage tool in Streamlit, used to manage message history within LLM-powered applications.
Key Features of StreamlitChatMessageHistory:
Stores chat messages within Streamlit's session state.
Not persistent across sessions; resets when the session is closed.
Specific to Streamlit applications, not designed for all LLM applications.
Why Option (D) is Incorrect:
StreamlitChatMessageHistory is designed for Streamlit-based apps.
It is not suitable for all LLM applications, particularly those requiring persistent storage.
Why Other Options Are Correct:
(A) True: Each session has its own instance of StreamlitChatMessageHistory.
(B) True: It is not persisted across sessions.
(C) True: It stores messages in the Streamlit session state.
🔹 Oracle Generative AI Reference:
While Oracle AI supports various LLM applications, StreamlitChatMessageHistory is limited to Streamlit-based chat interfaces.


NEW QUESTION # 30
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T- Few fine-tuning involves updating the weights of all layers in the model.
  • B. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
  • C. T-Few fine-tuning requires manual annotation of input-output pain.
  • D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

Answer: B


NEW QUESTION # 31
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It updates all the weights of the model uniformly.
  • B. It does not update any weights but restructures the model architecture.
  • C. It increases the training time as compared to Vanilla fine-tuning.
  • D. It selectively updates only a fraction of the model's weights.

Answer: D

Explanation:
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.


NEW QUESTION # 32
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

  • A. By optimizing GPIJ memory utilization for each model's unique para
  • B. By loading the entire model into G PU memory for efficient processing
  • C. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
  • D. By allocating separate GPUS for each model instance

Answer: C

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 # 33
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