Pass Exam With Full Sureness – 1Z0-1127-25 Dumps with 90 Questions [Q28-Q44]

admin 1Z0-1127-25 Oracle February 17, 2026

5/5 - (1 vote)

Pass Exam With Full Sureness – 1Z0-1127-25 Dumps with 90 Questions

Verified 1Z0-1127-25 dumps Q&As – 100% Pass from ValidExam

NO.28 What is LangChain?

A JavaScript library for natural language processing

A Python library for building applications with Large Language Models

A Java library for text summarization

A Ruby library for text generation

NO.29 When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

When the LLM already understands the topics necessary for text generation

When the LLM does not perform well on a task and the data for prompt engineering is too large

When the LLM requires access to the latest data for generating outputs

When you want to optimize the model without any instructions

NO.30 What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

Support for tokenizing longer sentences

Improved retrievals for Retrieval Augmented Generation (RAG) systems

Emphasis on syntactic clustering of word embeddings

Capacity to translate text in over 100 languages

NO.31 Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

Step-Back Prompting

Chain-of-Thought

Least-to-Most Prompting

In-Context Learning

NO.32 Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

Encoder-Decoder

Ranker

Generator

Retriever

NO.33 Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

LCEL is a programming language used to write documentation for LangChain.

LCEL is a legacy method for creating chains in LangChain.

LCEL is a declarative and preferred way to compose chains together.

LCEL is an older Python library for building Large Language Models.

NO.34 What does a cosine distance of 0 indicate about the relationship between two embeddings?

They are completely dissimilar

They are unrelated

They are similar in direction

They have the same magnitude

NO.35 Which is NOT a typical use case for LangSmith Evaluators?

Measuring coherence of generated text

Aligning code readability

Evaluating factual accuracy of outputs

Detecting bias or toxicity

NO.36 How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

NO.37 What does “Loss” measure in the evaluation of OCI Generative AI fine-tuned models?

The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

The improvement in accuracy achieved by the model during training on the user-uploaded dataset

The level of incorrectness in the model’s predictions, with lower values indicating better performance

NO.38 Which statement is true about string prompt templates and their capability regarding variables?

They can only support a single variable at a time.

They are unable to use any variables.

They support any number of variables, including the possibility of having none.

They require a minimum of two variables to function properly.

NO.39 What happens if a period (.) is used as a stop sequence in text generation?

The model ignores periods and continues generating text until it reaches the token limit.

The model generates additional sentences to complete the paragraph.

The model stops generating text after it reaches the end of the current paragraph.

The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.

NO.40 In which scenario is soft prompting appropriate compared to other training styles?

When there is a significant amount of labeled, task-specific data available

When the model needs to be adapted to perform well in a domain on which it was not originally trained

When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

When the model requires continued pretraining on unlabeled data

NO.41 What is the purpose of memory in the LangChain framework?

To retrieve user input and provide real-time output only

To store various types of data and provide algorithms for summarizing past interactions

To perform complex calculations unrelated to user interaction

To act as a static database for storing permanent records

NO.42 Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

“Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.”

“Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.”

“To understand the impact of greenhouse gases on climate change, let’s start by defining what greenhouse gases are. Next, we’ll explore how they trap heat in the Earth’s atmosphere.”A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

NO.43 Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

They always use an external database for generating responses.

They rely on internal knowledge learned during pretraining on a large text corpus.

They cannot generate responses without fine-tuning.

They use vector databases exclusively to produce answers.

NO.44 What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

It allows the LLM to access a larger dataset.

It eliminates the need for any training or computational resources.

It provides examples in the prompt to guide the LLM to better performance with no training cost.

It significantly reduces the latency for each model request.

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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 4	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.