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Guide for 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional

Quiz 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional

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/ 10

Quiz

1/10
What is the role of temperature in the decoding process of a Large Language Model (LLM)?
Select the answer
1 correct answer
A.
To increase the accuracy of the most likely word in the vocabulary
B.
To determine the number of words to generate in a single decoding step
C.
To decide to which part of speech the next word should belong
D.
To adjust the sharpness of probability distribution over vocabulary when selecting the next word

Quiz

2/10
Which statement accurately reflects the differences between these approaches in terms of the
number of parameters modified and the type of data used?
Select the answer
1 correct answer
A.
Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
B.
Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
C.
Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
D.
Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

Quiz

3/10
What is prompt engineering in the context of Large Language Models (LLMs)?
Select the answer
1 correct answer
A.
Iteratively refining the ask to elicit a desired response
B.
Adding more layers to the neural network
C.
Adjusting the hyperparameters of the model
D.
Training the model on a large dataset

Quiz

4/10
What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?
Select the answer
1 correct answer
A.
The model's ability to generate imaginative and creative content
B.
A technique used to enhance the model's performance on specific tasks
C.
The process by which the model visualizes and describes images in detail
D.
The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

Quiz

5/10
What does in-context learning in Large Language Models involve?
Select the answer
1 correct answer
A.
Pretraining the model on a specific domain
B.
Training the model using reinforcement learning
C.
Conditioning the model with task-specific instructions or demonstrations
D.
Adding more layers to the model

Quiz

6/10
What is the purpose of embeddings in natural language processing?
Select the answer
1 correct answer
A.
To increase the complexity and size of text data
B.
To translate text into a different language
C.
To create numerical representations of text that capture the meaning and relationships between words or phrases
D.
To compress text data into smaller files for storage

Quiz

7/10
What is the main advantage of using few-shot model prompting to customize a Large Language
Model (LLM)?
Select the answer
1 correct answer
A.
It allows the LLM to access a larger dataset.
B.
It eliminates the need for any training or computational resources.
C.
It provides examples in the prompt to guide the LLM to better performance with no training cost.
D.
It significantly reduces the latency for each model request.

Quiz

8/10
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?
Select the answer
1 correct answer
A.
GPUs are shared with other customers to maximize resource utilization.
B.
The GPUs allocated for a customer’s generative AI tasks are isolated from other GPUs.
C.
GPUs are used exclusively for storing large datasets, not for computation.
D.
Each customer's GPUs are connected via a public Internet network for ease of access.

Quiz

9/10
What happens if a period (.) is used as a stop sequence in text generation?
Select the answer
1 correct answer
A.
The model ignores periods and continues generating text until it reaches the token limit.
B.
The model generates additional sentences to complete the paragraph.
C.
The model stops generating text after it reaches the end of the current paragraph.
D.
The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.

Quiz

10/10
What is the purpose of frequency penalties in language model outputs?
Select the answer
1 correct answer
A.
To ensure that tokens that appear frequently are used more often
B.
To penalize tokens that have already appeared, based on the number of times they have been used
C.
To reward the tokens that have never appeared in the text
D.
To randomly penalize some tokens to increase the diversity of the text
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  • Quiz name:1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional
  • Total number of questions:88
  • Number of questions for the test:50
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