1Z0-1127-25 Exam Questions and Answers | Exam-Dumps.net (Page: 1)

QUESTION: 1

What is the role of temperature in the decoding process of a Large Language Model (LLM)?

To increase the accuracy of the most likely word in the vocabulary
To determine the number of words to generate in a single decoding step
To decide to which part of speech the next word should belong
To adjust the sharpness of probability distribution over vocabulary when selecting the next word

Answer(s): D

Explanation:

Comprehensive and Detailed In-Depth Explanation=
Temperature is a hyperparameter in the decoding process of LLMs that controls the randomness of word selection by modifying the probability distribution over the vocabulary. A lower temperature (e.g., 0.1) sharpens the distribution, making the model more likely to select the highest-probability words, resulting in more deterministic and focused outputs. A higher temperature (e.g., 2.0) flattens the distribution, increasing the likelihood of selecting less probable words, thus introducing more randomness and creativity. Option D accurately describes this role. Option A is incorrect because temperature doesn't directly increase accuracy but influences output diversity. Option B is unrelated, as temperature doesn't dictate the number of words generated. Option C is also incorrect, as part-of- speech decisions are not directly tied to temperature but to the model's learned patterns.

Reference:

General LLM decoding principles, likely covered in OCI 2025 Generative AI documentation under decoding parameters like temperature.

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QUESTION: 2

Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
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.
Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

Answer(s): C

Explanation:

Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't. : OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.

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QUESTION: 3

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

Iteratively refining the ask to elicit a desired response
Adding more layers to the neural network
Adjusting the hyperparameters of the model
Training the model on a large dataset

Answer(s): A

Explanation:

Comprehensive and Detailed In-Depth Explanation=
Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It's an iterative process that leverages the model's pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.
: OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.

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QUESTION: 4

What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

The model's ability to generate imaginative and creative content
A technique used to enhance the model's performance on specific tasks
The process by which the model visualizes and describes images in detail
The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

Answer(s): D

Explanation:

Comprehensive and Detailed In-Depth Explanation=
In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model's reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn't a performance- enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.
: OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.

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QUESTION: 5

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

Pretraining the model on a specific domain
Training the model using reinforcement learning
Conditioning the model with task-specific instructions or demonstrations
Adding more layers to the model

Answer(s): C

Explanation:

Comprehensive and Detailed In-Depth Explanation=
In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model's pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context. : OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt- based customization.

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QUESTION: 6

What is the purpose of embeddings in natural language processing?

To increase the complexity and size of text data
To translate text into a different language
To create numerical representations of text that capture the meaning and relationships between words or phrases
To compress text data into smaller files for storage

Answer(s): C

Explanation:

Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
: OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.

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QUESTION: 7

What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

It allows the LLM to access a larger dataset.
It eliminates the need for any training or computational resources.
It provides examples in the prompt to guide the LLM to better performance with no training cost.
It significantly reduces the latency for each model request.

Answer(s): C

Explanation:

Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting. : OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

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QUESTION: 8

Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

GPUs are shared with other customers to maximize resource utilization.
The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
GPUs are used exclusively for storing large datasets, not for computation.
Each customer's GPUs are connected via a public Internet network for ease of access.

Answer(s): B

Explanation:

Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
: OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

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Oracle 1Z0-1127-25 Exam (page: 1)
Oracle Cloud Infrastructure 2025 Generative AI Professional
Updated on: 11-Nov-2025

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Oracle 1Z0-1127-25 Exam (page: 1)
Oracle Cloud Infrastructure 2025 Generative AI Professional
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