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

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Oracle · 1Z0-1127-25

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Question 1

Single choice

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 selectively updates only a fraction of the model's weights.
D
It increases the training time as compared to Vanilla fine-tuning.

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Correct answerC

Question 2

Single choice

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A
Shared among multiple customers for efficiency
B
Stored in Object Storage encrypted by default
C
Stored in an unencrypted form in Object Storage
D
Stored in Key Management service

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Correct answerB

Question 3

Single choice

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

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

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Correct answerA

Question 4

Single choice

Which statement is true about string prompt templates and their capability regarding variables?

A
They can only support a single variable at a time.
B
They are unable to use any variables.
C
They support any number of variables, including the possibility of having none.
D
They require a minimum of two variables to function properly.

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Correct answerC

Question 5

Single choice

How does the structure of vector databases differ from traditional relational databases?

A
It stores data in a linear or tabular format.
B
It is not optimized for high-dimensional spaces.
C
It uses simple row-based data storage.
D
It is based on distances and similarities in a vector space.

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Correct answerD

Question 6

Single choice

What is prompt engineering in the context of Large Language Models (LLMs)?

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

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Correct answerA

Question 7

Single choice

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

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

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Correct answerA

Question 8

Single choice

What does the Ranker do in a text generation system?

A
It generates the final text based on the user's query.
B
It sources information from databases to use in text generation.
C
It evaluates and prioritizes the information retrieved by the Retriever.
D
It interacts with the user to understand the query better.

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Correct answerC

Question 9

Single choice

Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A
"Top p" selects tokens from the "Top k" tokens sorted by probability.
B
"Top p" assigns penalties to frequently occurring tokens.
C
"Top p" limits token selection based on the sum of their probabilities.
D
"Top p" determines the maximum number of tokens per response.

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Correct answerC

Question 10

Single choice

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A
Summarization models
B
Generation models
C
Translation models
D
Embedding models

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Correct answerC

Question 11

Single choice

How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A
Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B
Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
C
Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
D
Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

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Correct answerC

Question 12

Single choice

What does in-context learning in Large Language Models involve?

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

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Correct answerC

Question 13

Single choice

What do prompt templates use for templating in language model applications?

A
Python's list comprehension syntax
B
Python's str.format syntax
C
Python's lambda functions
D
Python's class and object structures

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Correct answerB

Question 14

Single choice

Why is normalization of vectors important before indexing in a hybrid search system?

A
It ensures that all vectors represent keywords only.
B
It significantly reduces the size of the database.
C
It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
D
It converts all sparse vectors to dense vectors.

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Correct answerC