Pass Your Oracle 1z0-1127-24 Exam with Correct 42 Questions and Answers [Q12-Q27]

29 Sep, 2024 By admin 1z0-1127-24, Oracle 0 Comments

Pass Your Oracle 1z0-1127-24 Exam with Correct 42 Questions and Answers [Q12-Q27]

4/5 - (1 vote)

Pass Your Oracle 1z0-1127-24 Exam with Correct 42 Questions and Answers

Latest [Sep 29, 2024] 2024 Realistic Verified 1z0-1127-24 Dumps

Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	Using OCI Generative AI Service: It covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
Topic 2	Fundamentals of Large Language Models (LLMs): This topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 3	Building an LLM Application with OCI Generative AI Service: The topic discusses 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.

NEW QUESTION 12
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

Improved retrievals for Retrieval Augmented Generation (RAG) systems

Capacity to translate text in over u languages

Support for tokenizing longer sentences

Emphasis on syntactic clustering of word embedding’s

NEW QUESTION 13
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A diffusion model that specializes in producing complex outputs.

A Large Language Model based agent that focuses on generating textual responses

A language model that operates on a token-by-token output basis

A Retrieval Augmented Generation (RAG) model that uses text as input and output

NEW QUESTION 14
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.

NEW QUESTION 15
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

RetrievalQA

Text Leader

Chain Deployment

GenerativeAI

NEW QUESTION 16
What is the primary purpose of LangSmith Tracing?

To monitor the performance of language models

To generate test cases for language models

To analyze the reasoning process of language

To debug issues in language model outputs

NEW QUESTION 17
What is the primary function of the “temperature” parameter in the OCI Generative AI Generation models?

Determines the maximum number of tokens the model can generate per response

Specifies a string that tells the model to stop generating more content

Assigns a penalty to tokens that have already appeared in the preceding text

Controls the randomness of the model’s output, affecting its creativity

NEW QUESTION 18
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

Retriever

Encoder-decoder

Ranker

Generator

NEW QUESTION 19
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.

NEW QUESTION 20
Which is a distinguishing feature of “Parameter-Efficient Fine-tuning (PEFT)” as opposed to classic Tine- tuning” in Large Language Model training?

PEFT involves only a few or new parameters and uses labeled, task-specific data.

PEFT modifies all parameters and uses unlabeled, task-agnostic data.

PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies

PEFT parameters and b typically used when no training data exists.

NEW QUESTION 21
Which role docs a “model end point” serve in the inference workflow of the OCI Generative AI service?

Hosts the training data for fine-tuning custom model

Evaluates the performance metrics of the custom model

Serves as a designated point for user requests and model responses

Updates the weights of the base model during the fine-tuning process

NEW QUESTION 22
In LangChain, which retriever search type is used to balance between relevancy and diversity?

mmr

similarity

similarity_score_threshold

top k

NEW QUESTION 23
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

NEW QUESTION 24
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

It leads to higher latency in model inference.

It enables the deployment of multiple fine-tuned models.

It limits the number of fine-tuned model deployable on the same GPU cluster.

It increases G PU memory requirements for model deployment.

NEW QUESTION 25
What is the purpose of the “stop sequence” parameter in the OCI Generative AI Generation models?

It com rob the randomness of the model* output, affecting its creativity.

It specifies a string that tells the model to stop generating more content

It assigns a penalty to frequently occurring tokens to reduce repetitive text.

It determines the maximum number of tokens the model can generate per response.

NEW QUESTION 26
When should you use the T-Few fine-tuning method for training a model?

For complicated semantical undemanding improvement

For models that require their own hosting dedicated Al duster

For data sets with a few thousand samples or less

For data sets with hundreds of thousands to millions of samples

NEW QUESTION 27
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.

Loading …

Get 2024 Updated Free Oracle 1z0-1127-24 Exam Questions and Answer: https://www.premiumvcedump.com/Oracle/valid-1z0-1127-24-premium-vce-exam-dumps.html

Premium VCE Dumps

Premium VCE Dumps

Pass Your Oracle 1z0-1127-24 Exam with Correct 42 Questions and Answers [Q12-Q27]

Oracle 1z0-1127-24 Exam Syllabus Topics:

Leave a Reply Cancel reply