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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q73-Q78):

NEW QUESTION # 73
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

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

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature controls the randomness of an LLM's output by adjusting the softmax probability distribution over the vocabulary. Increasing temperature (e.g., to 1.5) flattens the distribution, reducing the dominance of high-probability words and allowing more diverse, less predictable choices, making Option C correct. Option A is misleading-higher temperature doesn't remove the top word's impact entirely but reduces its relative likelihood. Option B is incorrect, as decreasing temperature sharpens the distribution, favoring likely words, not broadening it. Option D is false, as temperature directly affects the distribution, not just decoding speed. This mechanism is key for balancing creativity and coherence.
OCI 2025 Generative AI documentation likely explains temperature under decoding or output control parameters.

NEW QUESTION # 74
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
B. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
D. The level of incorrectness in the model's predictions, with lower values indicating better performance

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 75
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
B. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
C. PEFT modifies all parameters and is typically used when no training data exists.
D. PEFT modifies all parameters and uses unlabeled, task-agnostic data.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

NEW QUESTION # 76
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

A. It eliminates the need for any training or computational resources.
B. It provides examples in the prompt to guide the LLM to better performance with no training cost.
C. It significantly reduces the latency for each model request.
D. It allows the LLM to access a larger dataset.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting.
OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

NEW QUESTION # 77
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The proportion of incorrect predictions made by the model during an evaluation
B. The number of predictions a model makes, regardless of whether they are correct or incorrect
C. The depth of the neural network layers used in the model
D. How many predictions the model made correctly out of all the predictions in an evaluation

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

NEW QUESTION # 78
......

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