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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	OCI Generative AI サービスの使用: AI スペシャリスト向けのこのセクションでは、微調整と推論専用の AI クラスターについて説明します。このトピックでは、OCI Generative AI サービスの基礎、生成、要約、埋め込みの基本モデルにも重点を置いています。
トピック 2	大規模言語モデル (LLM) の基礎: AI 開発者とクラウドアーキテクト向けのこのトピックでは、LLM アーキテクチャと LLM の微調整について説明します。さらに、LLM のプロンプトとコードモデルの基礎にも重点を置いています。
トピック 3	OCI Generative AI サービスを使用した LLM アプリケーションの構築: AI エンジニア向けのこのセクションでは、検索拡張生成 (RAG) の概念、ベクターデータベースの概念、セマンティック検索の概念について説明します。また、LLM のデプロイ、LLM のトレースと評価、RAG と LangChain を使用した LLM アプリケーションの構築にも重点を置いています。

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Oracle Cloud Infrastructure 2024 Generative AI Professional 認定 1z0-1127-24 試験問題 (Q56-Q61):

質問 # 56
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Enhanced generalization to unseen data
B. Increased model interpretability
C. Reduced model complexity
D. Foster training time and lower cost

正解：D

質問 # 57
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It does not update any weights but restructures the model architecture.
B. It updates all the weights of the model uniformly.
C. It increases the training time as compared to Vanilla fine-tuning.
D. It selectively updates only a fraction of the model's weights.

正解：D

解説：
T-Few (Task-Specific Fine-tuning with Few-Shot Learning) is a fine-tuning approach designed to efficiently adapt Large Language Models (LLMs) to new tasks with minimal training data while using a small subset of model weights.
Characteristics of T-Few Fine-Tuning:
Selective Weight Updating: It does not update all model weights but focuses on a small fraction.
Few-Shot Learning Efficiency: Reduces the amount of labeled data required for fine-tuning.
Computational Cost Reduction: Requires significantly less compute than full model fine-tuning.
Better Transferability: Preserves the general knowledge of the base model while adapting to specific tasks.
Why Other Options Are Incorrect:
(B) is incorrect because T-Few updates weights rather than restructuring the model.
(C) is incorrect because not all weights are updated-only a small fraction.
(D) is incorrect because T-Few is optimized for efficiency and does not significantly increase training time.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient fine-tuning techniques like T-Few and LoRA (Low-Rank Adaptation) to enhance task-specific performance while reducing computational overhead.

質問 # 58
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
C. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
D. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

正解：D

質問 # 59
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It chooses words randomly from the set of less probable candidates.
B. It requires a large temperature setting to ensure diverse word selection.
C. It picks the most likely word email at each step of decoding.
D. It selects words bated on a flattened distribution over the vocabulary.

正解：C

解説：
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods

質問 # 60
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Capacity to translate text in over u languages
B. Support for tokenizing longer sentences
C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
D. Emphasis on syntactic clustering of word embedding's

正解：C

解説：
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems

質問 # 61
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1z0-1127-24最新な問題集、1z0-1127-24過去問

Oracle 1z0-1127-24 認定試験の出題範囲：

1z0-1127-24過去問 & 1z0-1127-24日本語独学書籍

Oracle Cloud Infrastructure 2024 Generative AI Professional 認定 1z0-1127-24 試験問題 (Q56-Q61):

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