TOP 1Z0-1127-25 QUESTIONS - UNLIMITED 1Z0-1127-25 EXAM PRACTICE

Top 1Z0-1127-25 Questions - Unlimited 1Z0-1127-25 Exam Practice

Top 1Z0-1127-25 Questions - Unlimited 1Z0-1127-25 Exam Practice

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
When does a chain typically interact with memory in a run within the LangChain framework?

  • A. Continuously throughout the entire chain execution process.
  • B. Before user input and after chain execution.
  • C. Only after the output has been generated.
  • D. After user input but before chain execution, and again after core logic but before output.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.


NEW QUESTION # 47
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

  • A. LangChain Application
  • B. LLMs
  • C. Vector Stores
  • D. Document Loaders

Answer: B

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 # 48
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When the LLM requires access to the latest data for generating outputs
  • B. When the LLM does not perform well on a task and the data for prompt engineering is too large
  • C. When you want to optimize the model without any instructions
  • D. When the LLM already understands the topics necessary for text generation

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


NEW QUESTION # 49
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

  • A. They rely on internal knowledge learned during pretraining on a large text corpus.
  • B. They cannot generate responses without fine-tuning.
  • C. They always use an external database for generating responses.
  • D. They use vector databases exclusively to produce answers.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.


NEW QUESTION # 50
What is LCEL in the context of LangChain Chains?

  • A. A legacy method for creating chains in LangChain
  • B. An older Python library for building Large Language Models
  • C. A programming language used to write documentation for LangChain
  • D. A declarative way to compose chains together using LangChain Expression Language

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.


NEW QUESTION # 51
......

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