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Peter Harris Peter Harris

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試験の準備方法-正確的な1z0-1127-24受験トレーリング試験-効率的な1z0-1127-24日本語版問題解説

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Oracle 1z0-1127-24 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

トピック 2

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

トピック 3

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers 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.

>> 1z0-1127-24受験トレーリング <<

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Oracle Cloud Infrastructure 2024 Generative AI Professional 認定 1z0-1127-24 試験問題 (Q13-Q18):

質問 # 13
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It determines the maximum number of tokens the model can generate per response.
B. It specifies a string that tells the model to stop generating more content
C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
D. It com rob the randomness of the model* output, affecting its creativity.

正解：B

解説：
The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.
Reference
OCI Generative AI service documentation
General principles of sequence generation in AI models

質問 # 14
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. Least to most Prompting
B. In context Learning
C. Chain-of-Through
D. Step-Bock Prompting

正解：C

解説：
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps

質問 # 15
When should you use the T-Few fine-tuning method for training a model?

A. For data sets with a few thousand samples or less
B. For complicated semantical undemanding improvement
C. For data sets with hundreds of thousands to millions of samples
D. For models that require their own hosting dedicated Al duster

正解：A

解説：
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models

質問 # 16
How does a presence penalty function in language model generation?

A. It penalizes only tokens that have never appeared in the text before.
B. It penalizes a token each time it appears after the first occurrence.
C. It applies a penalty only if the token has appeared more than twice.
D. It penalizes all tokens equally, regardless of how often they have appeared.

正解：B

解説：
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
🔹 Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.

質問 # 17
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By excluding transformer layers from the fine-tuning process entirely
B. By allowing updates across all layers of the model
C. By incorporating additional layers to the base model
D. By restricting updates to only a specific croup of transformer Layers

正解：D

質問 # 18
......

IT職員としてのあなたは昇進したいのですか。プロなIT技術専門家になりたいのですか。速くOracleの1z0-1127-24認定試験「Oracle Cloud Infrastructure 2024 Generative AI Professional」を申し込みましょう。この認証がどんなに重要するかあなたもよく知っています。試験に合格できないなんて心配しないで、あなたの能力を疑わないでください。Oracleの1z0-1127-24認定試験「Oracle Cloud Infrastructure 2024 Generative AI Professional」を受けたいのなら、試験の準備に関する全ての質問がJapancertは解決して差し上げます。JapancertはIT認証に対するプロなサイトです。Japancertがそばのいてあげたら、全ての難問が解決できます。Japancertに助けられた受験生は数え切れないです。Japancertをクロックしたら、１００パーセントの成功を差し上げます。

1z0-1127-24日本語版問題解説: https://www.japancert.com/1z0-1127-24.html

Peter Harris Peter Harris

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