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Oracle - 1Z0-1127-25 Updated Exam Quiz

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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.

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

NEW QUESTION # 47
Which is NOT a built-in memory type in LangChain?

A. ConversationTokenBufferMemory
B. ConversationBufferMemory
C. ConversationSummaryMemory
D. ConversationImageMemory

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

NEW QUESTION # 48
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

A. To adjust the sharpness of probability distribution over vocabulary when selecting the next word
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 decide to which part of speech the next word should belong

Answer: A

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 # 49
What is the function of the Generator in a text generation system?

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

Answer: A

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 # 50
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: A

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 # 51
What is the purpose of Retrievers in LangChain?

A. To train Large Language Models
B. To retrieve relevant information from knowledge bases
C. To combine multiple components into a single pipeline
D. To break down complex tasks into smaller steps

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.

NEW QUESTION # 52
......

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