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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q40-Q45):
NEW QUESTION # 40
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
Answer: C
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 # 41
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?
Answer: A
NEW QUESTION # 42
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?
Answer: D
Explanation:
Greedy Decoding is a simple and fast text generation strategy where the model always selects the word with the highest probability at each step.
How Greedy Decoding Works:
At each step of text generation, the model picks the most probable next word.
No consideration is given to long-term coherence or fluency.
This method can lead to repetitive and suboptimal outputs due to the lack of exploration.
Limitations of Greedy Decoding:
May miss optimal sentence structures because it only considers the next word, not the full sequence.
Less diversity in generated text, as it follows the highest-probability path rigidly.
Better alternatives exist: Beam Search, Top-k Sampling, and Temperature Scaling provide more refined results.
Why Other Options Are Incorrect:
(A) is incorrect because greedy decoding does not select random words.
(C) is incorrect because word choice is based on probability, not sentence structure.
(D) is incorrect because weighted random selection is used in sampling methods like Top-k or Top-p (nucleus sampling).
🔹 Oracle Generative AI Reference:
Oracle AI incorporates Greedy Decoding, Beam Search, and Stochastic Sampling in its text generation models to optimize for accuracy and diversity.
NEW QUESTION # 43
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?
Answer: D
Explanation:
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.
Reference
Research articles on sampling techniques in language models
Technical documentation for generative AI models in OCI
NEW QUESTION # 44
In which scenario is soft prompting appropriate compared to other training styles?
Answer: B
Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.
NEW QUESTION # 45
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