ORACLE 1Z0-1127-25 RELIABLE TEST PRACTICE - 1Z0-1127-25 PRACTICE TEST ENGINE

Oracle 1Z0-1127-25 Reliable Test Practice - 1Z0-1127-25 Practice Test Engine

Oracle 1Z0-1127-25 Reliable Test Practice - 1Z0-1127-25 Practice Test Engine

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q26-Q31):

NEW QUESTION # 26
How are prompt templates typically designed for language models?

  • A. As predefined recipes that guide the generation of language model prompts
  • B. As complex algorithms that require manual compilation
  • C. To work only with numerical data instead of textual content
  • D. To be used without any modification or customization

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 27
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

  • A. 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.
  • B. 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.
  • 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.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false-PEFT doesn't replace architecture. Option C is incorrect, as PEFT isn't trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.
OCI 2025 Generative AI documentation likely compares Fine-tuning and PEFT under customization techniques.
Here is the next batch of 10 questions (31-40) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 28
Why is it challenging to apply diffusion models to text generation?

  • A. Because diffusion models can only produce images
  • B. Because text is not categorical
  • C. Because text generation does not require complex models
  • D. Because text representation is categorical unlike images

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false-text generation can benefit from complex models. Option B is incorrect-text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.


NEW QUESTION # 29
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

  • A. It specifies a string that tells the model to stop generating more content.
  • B. It controls the randomness of the model's output, affecting its creativity.
  • C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
  • D. It determines the maximum number of tokens the model can generate per response.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "stop sequence" parameter defines a string (e.g., "." or "n") that, when generated, halts text generation, allowing control over output length or structure-Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.
OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.


NEW QUESTION # 30
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

  • A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
  • B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • C. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
  • D. PEFT modifies all parameters and is typically used when no training data exists.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.


NEW QUESTION # 31
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