無料1Z0-1127-25サンプル問題で100%カバー率のリアル試験問題（更新された90問あります) [Q54-Q78]

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無料1Z0-1127-25サンプル問題で100%カバー率のリアル試験問題（更新された90問あります)

今すぐダウンロード！リアルOracle 1Z0-1127-25試験問題集テストエンジン試験問題

54、Which statement describes the difference between “Top k” and “Top p” in selecting the next token in the OCI Generative AI Generation models?

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

“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 tokens.

“Top k” considers the sum of probabilities of the top tokens, whereas “Top p” 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.

55、When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

When the LLM already understands the topics necessary for text generation

When the LLM does not perform well on a task and the data for prompt engineering is too large

When the LLM requires access to the latest data for generating outputs

When you want to optimize the model without any instructions

56、Given the following code:
PromptTemplate(input_variables=[“human_input”, “city”], template=template) Which statement is true about PromptTemplate in relation to input_variables?

PromptTemplate requires a minimum of two variables to function properly.

PromptTemplate can support only a single variable at a time.

PromptTemplate supports any number of variables, including the possibility of having none.

PromptTemplate is unable to use any variables.

57、What is the primary purpose of LangSmith Tracing?

To generate test cases for language models

To analyze the reasoning process of language models

To debug issues in language model outputs

To monitor the performance of language models

58、What does accuracy measure in the context of fine-tuning results for a generative model?

The number of predictions a model makes, regardless of whether they are correct or incorrect

The proportion of incorrect predictions made by the model during an evaluation

How many predictions the model made correctly out of all the predictions in an evaluation

The depth of the neural network layers used in the model

59、How does the structure of vector databases differ from traditional relational databases?

It stores data in a linear or tabular format.

It is not optimized for high-dimensional spaces.

It uses simple row-based data storage.

It is based on distances and similarities in a vector space.

60、Which role does a “model endpoint” serve in the inference workflow of the OCI Generative AI service?

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

Serves as a designated point for user requests and model responses

Evaluates the performance metrics of the custom models

Hosts the training data for fine-tuning custom models

61、What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

Overfitting

Underfitting

Data Leakage

Model Drift

62、When should you use the T-Few fine-tuning method for training a model?

For complicated semantic understanding improvement

For models that require their own hosting dedicated AI cluster

For datasets with a few thousand samples or less

For datasets with hundreds of thousands to millions of samples

63、How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

64、Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.

Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.

Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.

Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

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

Retriever

Encoder-Decoder

Generator

Ranker

66、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:

“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.”

“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.”

“To understand the impact of greenhouse gases on climate change, let’s start by defining what greenhouse gases are. Next, we’ll explore how they trap heat in the Earth’s atmosphere.”A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

67、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

68、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.

69、What does “k-shot prompting” refer to when using Large Language Models for task-specific applications?

Providing the exact k words in the prompt to guide the model’s response

Explicitly providing k examples of the intended task in the prompt to guide the model’s output

The process of training the model on k different tasks simultaneously to improve its versatility

Limiting the model to only k possible outcomes or answers for a given task

70、How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Shared among multiple customers for efficiency

Stored in Object Storage encrypted by default

Stored in an unencrypted form in Object Storage

Stored in Key Management service

71、In the simplified workflow for managing and querying vector data, what is the role of indexing?

To convert vectors into a non-indexed format for easier retrieval

To map vectors to a data structure for faster searching, enabling efficient retrieval

To compress vector data for minimized storage usage

To categorize vectors based on their originating data type (text, images, audio)

72、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” selects tokens from the “Top k” tokens sorted by probability.

“Top p” determines the maximum number of tokens per response.

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

73、Why is it challenging to apply diffusion models to text generation?

Because text generation does not require complex models

Because text is not categorical

Because text representation is categorical unlike images

Because diffusion models can only produce images

74、How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

Increasing temperature removes the impact of the most likely word.

Decreasing temperature broadens the distribution, making less likely words more probable.

Increasing temperature flattens the distribution, allowing for more varied word choices.

Temperature has no effect on the probability distribution; it only changes the speed of decoding.

75、What does the term “hallucination” refer to in the context of Large Language Models (LLMs)?

The model’s ability to generate imaginative and creative content

A technique used to enhance the model’s performance on specific tasks

The process by which the model visualizes and describes images in detail

The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

76、What does the Loss metric indicate about a model’s predictions?

Loss measures the total number of predictions made by a model.

Loss is a measure that indicates how wrong the model’s predictions are.

Loss indicates how good a prediction is, and it should increase as the model improves.

Loss describes the accuracy of the right predictions rather than the incorrect ones.

77、What does a higher number assigned to a token signify in the “Show Likelihoods” feature of the language model token generation?

The token is less likely to follow the current token.

The token is more likely to follow the current token.

The token is unrelated to the current token and will not be used.

The token will be the only one considered in the next generation step.

78、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 based on a flattened distribution over the vocabulary.

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

Oracle 1Z0-1127-25 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.
トピック 2	Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI’s security architecture for generative AI and emphasizes responsible AI practices.
トピック 3	Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.
トピック 4	Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.