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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
How are chains traditionally created in LangChain?

A. Using Python classes, such as LLMChain and others
B. By using machine learning algorithms
C. Exclusively through third-party software integrations
D. Declaratively, with no coding required

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define sequences of operations, such as calling an LLM or processing data. This programmatic approach predates LCEL's declarative style, making Option C correct. Option A is vague and incorrect, as chains aren't ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false, as third-party integrations aren't required. Python classes provide structured chain building.
OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under LangChain sections.

NEW QUESTION # 14
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Generation models
B. Embedding models
C. Summarization models
D. Translation models

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

NEW QUESTION # 15
What does the RAG Sequence model do in the context of generating a response?

A. It modifies the input query before retrieving relevant documents to ensure a diverse response.
B. It retrieves relevant documents only for the initial part of the query and ignores the rest.
C. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
D. It retrieves a single relevant document for the entire input query and generates a response based on that alone.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect-RAG considers the full query. Option D is false-query modification isn't standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.

NEW QUESTION # 16
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

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

Answer: C

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 # 17
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. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
C. The model's ability to generate imaginative and creative content
D. A technique used to enhance the model's performance on specific tasks

Answer: B

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 # 18
......

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Exam 1Z0-1127-25 Forum, 1Z0-1127-25 Latest Exam Duration

100% Pass Quiz Oracle 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional High Hit-Rate Exam Forum

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q13-Q18):

1Z0-1127-25 Learning Material: Oracle Cloud Infrastructure 2025 Generative AI Professional & 1Z0-1127-25 Practice Test

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