1Z0-1127-25 ACCURATE TEST - FLEXIBLE 1Z0-1127-25 TESTING ENGINE

1Z0-1127-25 Accurate Test - Flexible 1Z0-1127-25 Testing Engine

1Z0-1127-25 Accurate Test - Flexible 1Z0-1127-25 Testing Engine

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
What is the purpose of memory in the LangChain framework?

  • A. To store various types of data and provide algorithms for summarizing past interactions
  • B. To perform complex calculations unrelated to user interaction
  • C. To act as a static database for storing permanent records
  • D. To retrieve user input and provide real-time output only

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.


NEW QUESTION # 22
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 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. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

Answer: B

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 # 23
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

  • A. Retriever
  • B. Generator
  • C. Ranker
  • D. Encoder-Decoder

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


NEW QUESTION # 24
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

  • A. Increasing the temperature removes the impact of the most likely word.
  • B. Temperature has no effect on probability distribution; it only changes the speed of decoding.
  • C. Decreasing the temperature broadens the distribution, making less likely words more probable.
  • D. Increasing the temperature flattens the distribution, allowing for more varied word choices.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.


NEW QUESTION # 25
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

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

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


NEW QUESTION # 26
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