[Sep 11, 2024] Get Unlimited Access to 1z0-1127-24 Certification Exam Cert Guide [Q12-Q33]

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[Sep 11, 2024] Get Unlimited Access to 1z0-1127-24 Certification Exam Cert Guide

Reliable Study Materials for 1z0-1127-24 Exam Success For Sure

NO.12 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.13 Which is a key characteristic of the annotation process used in T-Few fine-tuning?

T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

T-Few fine-tuning requires manual annotation of input-output pain.

T- Few fine-tuning involves updating the weights of all layers in the model.

T-Few fine-tuning relies on unsupervised learning techniques for annotation.

NO.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.

NO.15 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.

NO.16 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.17 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.18 Given the following code: chain = prompt |11m

Which statement is true about LangChain Expression language (ICED?

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.

NO.19 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.20 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 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 data set

The level of incorrectness in the models predictions, with lower values indicating better performance

NO.21 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.22 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.23 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.24 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.25 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.26 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.27 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

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