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[Hardware] Reliable Oracle 1Z0-1127-25 Exam Bootcamp & Reliable 1Z0-1127-25 Exam Pdf

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【Hardware】 Reliable Oracle 1Z0-1127-25 Exam Bootcamp & Reliable 1Z0-1127-25 Exam Pdf

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):NEW QUESTION # 46
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 # 47
What is the purpose of embeddings in natural language processing?
  • A. To create numerical representations of text that capture the meaning and relationships between words or phrases
  • B. To increase the complexity and size of text data
  • C. To translate text into a different language
  • D. To compress text data into smaller files for storage
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.

NEW QUESTION # 48
Which LangChain component is responsible for generating the linguistic output in a chatbot system?
  • A. LLMs
  • B. Vector Stores
  • C. LangChain Application
  • D. Document Loaders
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, LLMs (Large Language Models) generate the linguistic output (text responses) in a chatbot system, leveraging their pre-trained capabilities. This makes Option D correct. Option A (Document Loaders) ingests data, not generates text. Option B (Vector Stores) manages embeddings for retrieval, not generation. Option C (LangChain Application) is too vague-it's the system, not a specific component. LLMs are the core text-producing engine.
OCI 2025 Generative AI documentation likely identifies LLMs as the generation component in LangChain.

NEW QUESTION # 49
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
  • A. Generator
  • B. Encoder-Decoder
  • C. Retriever
  • D. Ranker
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 50
What is the purpose of memory in the LangChain framework?
  • A. To act as a static database for storing permanent records
  • B. To perform complex calculations unrelated to user interaction
  • C. To store various types of data and provide algorithms for summarizing past interactions
  • D. To retrieve user input and provide real-time output only
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.

NEW QUESTION # 51
......
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