AUTHORITATIVE 1Z0-1127-25 VALID TEST DUMPS TO OBTAIN ORACLE CERTIFICATION

Authoritative 1Z0-1127-25 Valid Test Dumps to Obtain Oracle Certification

Authoritative 1Z0-1127-25 Valid Test Dumps to Obtain Oracle Certification

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

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

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 # 67
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. "Top p" determines the maximum number of tokens per response.
  • B. "Top p" assigns penalties to frequently occurring tokens.
  • C. "Top p" limits token selection based on the sum of their probabilities.
  • D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) 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 # 68
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

  • A. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
  • B. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
  • C. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.


NEW QUESTION # 69
In the simplified workflow for managing and querying vector data, what is the role of indexing?

  • A. To compress vector data for minimized storage usage
  • B. To categorize vectors based on their originating data type (text, images, audio)
  • C. To convert vectors into a non-indexed format for easier retrieval
  • D. To map vectors to a data structure for faster searching, enabling efficient retrieval

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.


NEW QUESTION # 70
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It does not update any weights but restructures the model architecture.
  • B. It increases the training time as compared to Vanilla fine-tuning.
  • C. It selectively updates only a fraction of the model's weights.
  • D. It updates all the weights of the model uniformly.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.


NEW QUESTION # 71
......

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