Free 1z0-1127-24 Exam Braindumps - New 2025 Oracle Pratice Exam [Q29-Q44]

Free 1z0-1127-24 Exam Braindumps – New 2025 Oracle Pratice Exam [Q29-Q44]

November 14, 2025 admin 0 Comments

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Free 1z0-1127-24 Exam Braindumps – New 2025 Oracle Pratice Exam

Practice Test for 1z0-1127-24 Certification Real 2025 Mock Exam

Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
Topic 2	Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
Topic 3	Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

QUESTION 29
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

Retriever

Encoder-decoder

Ranker

Generator

QUESTION 30
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

Translation models

Summarization models

Generation models

Embedding models

In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI

QUESTION 32
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

Continuously throughout the entire chain execution process

Only after the output has been generated

After user input but before chain execution, and again after core logic but before output

Before user input and after chain execution

QUESTION 34
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

They require frequent manual updates, which increase operational costs.

They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.

They increase the cost due to the need for real- time updates.

They are more expensive but provide higher quality data.

Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamic retrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.
Reference
Articles on the cost efficiency of vector databases
Research on integrating vector databases with LLMs for real-time updates

QUESTION 37
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.
L 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.
2. 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.
3. To understand the impact of greenhouse gases on climate change, let’s start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most

1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back

1:Chain-of-Thought ,2:Step-Back, 3:Least-to most

1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.
Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.
Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.
Reference
Research articles on prompting techniques for language models
Documentation on effective use of prompting strategies

QUESTION 38
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

It transforms their architecture from a neural network to a traditional database system.

It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.

It enables them to bypass the need for pretraining on large text corpora.

It limits their ability to understand and generate natural language.

QUESTION 39
Which statement best describes the role of encoder and decoder models in natural language processing?

Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.

Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.

Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

QUESTION 40
In LangChain, which retriever search type is used to balance between relevancy and diversity?

mmr

similarity

similarity_score_threshold

top k

QUESTION 41
Which is NOT a built-in memory type in LangChain?

Conversation Token Buffer Memory

Conversation ImgeMemory

Conversation Buffer Memory

Conversation Summary Memory

In LangChain, “Conversation Image Memory” is not a built-in memory type. The built-in memory types in LangChain include:
Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.
Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.
Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.
These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.
Reference
LangChain documentation on memory types
Technical guides on implementing memory in conversational AI systems

QUESTION 42
Which is the main characteristic of greedy decoding in the context of language model word prediction?

It chooses words randomly from the set of less probable candidates.

It requires a large temperature setting to ensure diverse word selection.

It selects words bated on a flattened distribution over the vocabulary.

It picks the most likely word email at each step of decoding.

QUESTION 44
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

By incorporating additional layers to the base model

By allowing updates across all layers of the model

By excluding transformer layers from the fine-tuning process entirely

By restricting updates to only a specific croup of transformer Layers

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