100% PASS ORACLE - 1Z0-1127-25 - TRUSTABLE LATEST ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL TEST PRACTICE

100% Pass Oracle - 1Z0-1127-25 - Trustable Latest Oracle Cloud Infrastructure 2025 Generative AI Professional Test Practice

100% Pass Oracle - 1Z0-1127-25 - Trustable Latest Oracle Cloud Infrastructure 2025 Generative AI Professional Test Practice

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q82-Q87):

NEW QUESTION # 82
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. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
  • 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. 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 # 83
What is prompt engineering in the context of Large Language Models (LLMs)?

  • A. Iteratively refining the ask to elicit a desired response
  • B. Training the model on a large dataset
  • C. Adding more layers to the neural network
  • D. Adjusting the hyperparameters of the model

Answer: 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.


NEW QUESTION # 84
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?

  • A. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.
  • B. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
  • C. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
  • D. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top k" sampling selects from the k most probable tokens, based on their ranked position, while "Top p" (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass-Option B is correct. Option A is false-they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect-both use probability, not frequency. This distinction affects diversity.
OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.


NEW QUESTION # 85
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When the LLM requires access to the latest data for generating outputs
  • B. When the LLM does not perform well on a task and the data for prompt engineering is too large
  • C. When you want to optimize the model without any instructions
  • D. When the LLM already understands the topics necessary for text generation

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


NEW QUESTION # 86
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

  • A. 480 unit hours
  • B. 744 unit hours
  • C. 20 unit hours
  • D. 240 unit hours

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.


NEW QUESTION # 87
......

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