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Oracle Cloud Infrastructure 2024 Generative AI Professional - 1z0-1127-24 Exam Practice Test

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Total 66 questions

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Question 1

Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

A. Chain Deployment B. RetrievalQA C. Text Leader D. GenerativeAI

Correct Answer: D

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Question 2

What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

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

Correct Answer: D

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Question 3

How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the presence and frequency of the user-provided keywords B. By the complexity of language used in the documents C. Based on the number of images and videos contained in the documents D. According to the length of the documents

Correct Answer: A

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Question 4

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

A. To retrieve text from an external source and present it without any modifications B. To generate text based only on the model's internal knowledge without external data C. To generate text using extra information obtained from an external data source D. To store text in an external database without using it for generation

Correct Answer: C

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Question 5

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 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. C. 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. 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.

Correct Answer: B

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Question 6

An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A. A Retrieval Augmented Generation (RAG) model that uses text as input and output B. A Large Language Model based agent that focuses on generating textual responses C. A language model that operates on a token-by-token output basis D. A diffusion model that specializes in producing complex outputs.

Correct Answer: D

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Question 7

In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To compress vector data for minimized storage usage B. To convert vectors into a nonindexed format for easier retrieval C. To categorize vectors based on their originating data type (text, images, audio) D. To map vectors to a data structure for faster searching, enabling efficient retrieval

Correct Answer: D

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Total 66 questions

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