VALID 1Z0-1127-25 BRAINDUMPS, PASS4SURE 1Z0-1127-25 PASS GUIDE

Valid 1Z0-1127-25 Braindumps, Pass4sure 1Z0-1127-25 Pass Guide

Valid 1Z0-1127-25 Braindumps, Pass4sure 1Z0-1127-25 Pass Guide

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ExamDumpsVCE's Oracle 1Z0-1127-25 practice exam software tracks your performance and provides results on the spot about your attempt. In this way, our Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) simulation software encourages self-analysis and self-improvement. Questions in the Oracle 1Z0-1127-25 Practice Test software bear a striking resemblance to those of the real test.

The Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) certification test is an important part of career growth, and passing it may lead to more employment opportunities. However, preparing for the Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) test may be tough, and many busy applicants have difficulty cracking it. This is where ExamDumpsVCE Oracle Cloud Infrastructure 2025 Generative AI Professional real exam questions come to help you clear the test in a short time.

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These 1Z0-1127-25 exam question formats contain real, valid, and updated Oracle 1Z0-1127-25 exam questions that will assist you in Oracle Oracle Cloud Infrastructure 2025 Generative AI Professional exam preparation and enable you to pass the challenging Oracle 1Z0-1127-25 Exam with good scores. The Oracle 1Z0-1127-25 questions are prepared by highly experienced professionals and, thus, are kept to the point and concise.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It chooses words randomly from the set of less probable candidates.
  • B. It requires a large temperature setting to ensure diverse word selection.
  • C. It picks the most likely word at each step of decoding.
  • D. It selects words based on a flattened distribution over the vocabulary.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.


NEW QUESTION # 48
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" limits token selection based on the sum of their probabilities.
  • C. "Top p" assigns penalties to frequently occurring tokens.
  • D. "Top p" selects tokens from the "Top k" tokens sorted by probability.

Answer: B

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 # 49
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

  • A. LCEL is a legacy method for creating chains in LangChain.
  • B. LCEL is a programming language used to write documentation for LangChain.
  • C. LCEL is an older Python library for building Large Language Models.
  • D. LCEL is a declarative and preferred way to compose chains together.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain Expression Language (LCEL) is a declarative syntax (e.g., using | to pipe components) for composing chains in LangChain, combining prompts, LLMs, and other elements efficiently-Option C is correct. Option A is false-LCEL isn't for documentation. Option B is incorrect-it's current, not legacy; traditional Python classes are older. Option D is wrong-LCEL is part of LangChain, not a standalone LLM library. LCEL simplifies chain design.
OCI 2025 Generative AI documentation likely highlights LCEL under LangChain chaincomposition.


NEW QUESTION # 50
What is prompt engineering in the context of Large Language Models (LLMs)?

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

Answer: B

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 # 51
What is the purpose of frequency penalties in language model outputs?

  • A. To randomly penalize some tokens to increase the diversity of the text
  • B. To penalize tokens that have already appeared, based on the number of times they have been used
  • C. To reward the tokens that have never appeared in the text
  • D. To ensure that tokens that appear frequently are used more often

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Frequency penalties reduce the likelihood of repeating tokens that have already appeared in the output, based on their frequency, to enhance diversity and avoid repetition. This makes Option B correct. Option A is the opposite effect. Option C describes a different mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties aren't random but frequency-based.
OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters.
Below is the next batch of 10 questions (11-20) from your list, formatted as requested with detailed explanations. These 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 # 52
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