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[General] Quiz 2026 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Profession

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【General】 Quiz 2026 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Profession

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.
Topic 2
  • Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.
Topic 3
  • Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.
Topic 4
  • Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.

Quiz 2026 Oracle Updated 1Z0-1127-25: Exam Oracle Cloud Infrastructure 2025 Generative AI Professional PreviewNowadays, the certification has been one of the criteria for many companies to recruit employees. And in order to obtain the 1Z0-1127-25 certification, taking the 1Z0-1127-25 exam becomes essential. Although everyone hopes to pass the exam, the difficulties in preparing for it should not be overlooked. There are plenty of people who took a lot of energy and time but finally failed to pass. You really need our 1Z0-1127-25 practice materials which can work as the pass guarantee.
Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q83-Q88):NEW QUESTION # 83
What do prompt templates use for templating in language model applications?
  • A. Python's str.format syntax
  • B. Python's list comprehension syntax
  • C. Python's class and object structures
  • D. Python's lambda functions
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

NEW QUESTION # 84
What happens if a period (.) is used as a stop sequence in text generation?
  • A. The model ignores periods and continues generating text until it reaches the token limit.
  • B. The model stops generating text after it reaches the end of the current paragraph.
  • C. The model generates additional sentences to complete the paragraph.
  • D. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence's purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.
OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.

NEW QUESTION # 85
What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?
  • A. The process by which the model visualizes and describes images in detail
  • B. A technique used to enhance the model's performance on specific tasks
  • C. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
  • D. The model's ability to generate imaginative and creative content
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model's reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn't a performance-enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.
OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.

NEW QUESTION # 86
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?
  • A. Selecting a random word from the entire vocabulary at each step
  • B. Picking a word based on its position in a sentence structure
  • C. Using a weighted random selection based on a modulated distribution
  • D. Choosing the word with the highest probability at each step of decoding
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn't how greedy decoding works-it's probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.
OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.

NEW QUESTION # 87
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 determine the number of words to generate in a single decoding step
  • C. To increase the accuracy of the most likely word in the vocabulary
  • 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 # 88
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