Oracle - 1Z0-1127-25 - Newest Oracle Cloud Infrastructure 2025 Generative AI Professional Latest Exam Question

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	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 4	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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which is NOT a typical use case for LangSmith Evaluators?

A. Evaluating factual accuracy of outputs
B. Aligning code readability
C. Detecting bias or toxicity
D. Measuring coherence of generated text

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

NEW QUESTION # 12
What is the function of the Generator in a text generation system?

A. To store the generated responses for future use
B. To collect user queries and convert them into database search terms
C. To rank the information based on its relevance to the user's query
D. To generate human-like text using the information retrieved and ranked, along with the user's original query

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.

NEW QUESTION # 13
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

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

Answer: C

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 # 14
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Emphasis on syntactic clustering of word embeddings
B. Improved retrievals for Retrieval Augmented Generation (RAG) systems
C. Support for tokenizing longer sentences
D. Capacity to translate text in over 100 languages

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.

NEW QUESTION # 15
In which scenario is soft prompting appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a domain on which it was not originally trained
B. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
C. When the model requires continued pretraining on unlabeled data
D. When there is a significant amount of labeled, task-specific data available

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

NEW QUESTION # 16
......

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Oracle - 1Z0-1127-25 - Newest Oracle Cloud Infrastructure 2025 Generative AI Professional Latest Exam Question

Oracle 1Z0-1127-25 Latest Braindumps Free & 1Z0-1127-25 Free Brain Dumps

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q11-Q16):

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