[Q15-Q37] Pass Oracle 1z0-1127-24 Exam in First Attempt Guaranteed [Sep-2024]

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Pass Oracle 1z0-1127-24 Exam in First Attempt Guaranteed [Sep-2024]

Exam Sure Pass Oracle Certification with 1z0-1127-24 exam questions

NEW QUESTION 15
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

Step-Bock Prompting

Chain-of-Through

Least to most Prompting

In context Learning

NEW QUESTION 16
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.

NEW QUESTION 17
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.

NEW QUESTION 18
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

NEW QUESTION 19
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

Translation models

Summarization models

Generation models

Embedding models

NEW QUESTION 20
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 21
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

10 unit hours

30 unit hours

15 unit hours

40 unit hours

NEW QUESTION 22
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model’s response?

Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.

RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.

RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

NEW QUESTION 23
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 24
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

NEW QUESTION 25
Which statement is true about the “Top p” parameter of the OCI Generative AI Generation models?

Top p assigns penalties to frequently occurring tokens.

Top p determines the maximum number of tokens per response.

Top p limits token selection based on the sum of their probabilities.

Top p selects tokens from the “Top k’ tokens sorted by probability.

NEW QUESTION 26
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

By sharing base model weights across multiple fine-tuned model’s on the same group of GPUs

By optimizing GPIJ memory utilization for each model’s unique para

By allocating separate GPUS for each model instance

By loading the entire model into G PU memory for efficient processing

NEW QUESTION 27
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 28
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.

NEW QUESTION 29
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 30
Which is NOT a built-in memory type in LangChain?

Conversation Token Buffer Memory

Conversation ImgeMemory

Conversation Buffer Memory

Conversation Summary Memory

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[Q15-Q37] Pass Oracle 1z0-1127-24 Exam in First Attempt Guaranteed [Sep-2024]

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