100% Free Real Updated 1z0-1127-24 Questions & Answers Pass Your Exam Easily [Q24-Q41]

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100% Free Real Updated 1z0-1127-24 Questions & Answers Pass Your Exam Easily

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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.

NO.24 Given the following code:
Prompt Template
(input_variable[”rhuman_input”,’city”], template-template)
Which statement is true about Promt Template in relation to input_variables?

PromptTemplate requires a minimum of two variables to function property.

PromptTemplate can support only a single variable M a time.

PromptTemplate supports Any number of variable*, including the possibility of having none.

PromptTemplate is unable to use any variables.

NO.25 Why is normalization of vectors important before indexing in a hybrid search system?

It converts all sparse vectors to dense vectors.

It significantly reduces the size of the database.

It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

It ensures that all vectors represent keywords only.

NO.26 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.

NO.27 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

NO.28 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.

NO.29 How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Stored in Object Storage encrypted by default

Shared among multiple customers for efficiency

Stored in Key Management service

Stored in an unencrypted form in Object Storage

NO.30 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

NO.31 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

NO.32 Which statement describes the difference between Top V and Top p” in selecting the next token in the OCI Generative AI Generation models?

Top k selects the next token based on its position in the list of probable tokens, whereas “Top p” selects based on the cumulative probability of the Top token.

Top K considers the sum of probabilities of the top tokens, whereas Top” selects from the Top k” tokens sorted by probability.

Top k and Top p” both select from the same set of tokens but use different methods to prioritize them based on frequency.

Top k and “Top p” are identical in their approach to token selection but differ in their application of penalties to tokens.

NO.33 What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

Overfilling

Underfitting

Data Leakage

Model Drift

NO.34 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

NO.35 Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

Reduced model complexity

Enhanced generalization to unseen data

Increased model interpretability

Foster training time and lower cost

NO.36 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

NO.37 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.

NO.38 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

NO.39 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.

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