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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

Given the following code: chain = prompt |11m

A. LCEL is a declarative and preferred way to compose chains together. B. LCEL is a programming language used to write documentation for LangChain. C. Which statement is true about LangChain Expression language (ICED? D. LCEL is a legacy method for creating chains in LangChain

Correct Answer: A

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Question 2

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

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

Correct Answer: B

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Question 3

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

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

Correct Answer: B

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Question 4

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

A. By loading the entire model into G PU memory for efficient processing B. By optimizing GPIJ memory utilization for each model's unique para C. By allocating separate GPUS for each model instance D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs

Correct Answer: D

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Question 5

Which is NOT a typical use case for LangSmith Evaluators?

A. Aliening code readability B. Measuring coherence of generated text C. Detecting bias or toxicity D. Evaluating factual accuracy of outputs

Correct Answer: A

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Question 6

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. Text Leader C. GenerativeAI D. RetrievalQA

Correct Answer: C

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Question 7

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

A. 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. 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 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.

Correct Answer: D

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Total 66 questions

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