Free Oracle 1Z0-1127-25 Exam Questions

Become Oracle Certified with updated 1Z0-1127-25 exam questions and correct answers

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Total 234 Questions | Updated On: Jun 24, 2025

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Question 1

Given the following code: Prompt Template (input_variable[‘’rhuman_input",'city’’], template-template) Which statement is true about Promt Template in relation to input_variables?

A : PromptTemplate requires a minimum of two variables to function property.

B : PromptTemplate can support only a single variable M a time.

C : PromptTemplate supports Any number of variable*, including the possibility of having none.

D : PromptTemplate is unable to use any variables.

Answer: C

Question 2

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

A : When the LLM requires access to the latest data for generating outputs

B : When the LLM already understands the topics necessary for text generation

C : When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: C

Question 3

Multi-modal LLMs can process different data types. Which of the following combinations is NOT a typical example of multi-modal data?

A : Text and image data

B : Text and audio data

C : Text and code data

D : Text data only

Answer: D

Question 4

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A : Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

B : Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.

C : Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.

D : PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.

Answer: C

Question 5

How does the architecture of dedicated AI clusters contribute to minimizing GPU memory overhead for T-Few fine-tuned model inference?

A : By sharing base model weights across multiple fine-tuned models on the same group of GPUs

B : By optimizing GPU memory utilization for each model’s unique parameters

C : By allocating separate GPUs for each model instance

D : By loading the entire model into GPU memory for efficient processing

Answer: A

Page: 1 / 47

Total 234 Questions | Updated On: Jun 24, 2025

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