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[General] 1Z0-1127-25資格難易度 & 1Z0-1127-25対策学習

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【General】 1Z0-1127-25資格難易度 & 1Z0-1127-25対策学習

Posted at yesterday 05:41      View：16 | Replies：0        Print      Only Author   [Copy Link] 1#
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あなたは彼と同じような仕事の能力を持っていると思うかもしれませんし、あなたも一生懸命働いているので、誰かが突然昇進していることに気付きましたか？ （1Z0-1127-25信頼できる試験ダンプ）有効なOracle認定が鍵になるかもしれません。 あなたの会社がこの大企業のプロジェクトに応募する場合、有用な認定はプロジェクトマネージャーの地位にとって大きな利点になります。 1Z0-1127-25信頼できる試験ダンプは、試験に合格し、貴重な変更を取得するのに役立ちます。 heしないでください。 時は金なり。 当社の1Z0-1127-25信頼できる試験ダンプは、近年、数千人の受験者が試験をクリアするのに役立ちました。
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
  • 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 2
  • 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.
トピック 3
  • 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 4
  • 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.

真実的な 1Z0-1127-25資格難易度 & 保証するOracle 1Z0-1127-25 最高の試験の成功1Z0-1127-25対策学習Oracle 1Z0-1127-25試験の困難度なので、試験の準備をやめます。実には、正確の方法と資料を探すなら、すべては問題ではりません。我々社はOracle 1Z0-1127-25試験に準備するあなたに怖さを取り除き、正確の方法と問題集を提供できます。ご購入の前後において、いつまでもあなたにヘルプを与えられます。あなたのOracle 1Z0-1127-25試験に合格するのは我々が与えるサプライズです。
Oracle Cloud Infrastructure 2025 Generative AI Professional 認定 1Z0-1127-25 試験問題 (Q13-Q18):質問 # 13
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
  • A. "Top p" assigns penalties to frequently occurring tokens.
  • B. "Top p" limits token selection based on the sum of their probabilities.
  • C. "Top p" determines the maximum number of tokens per response.
  • D. "Top p" selects tokens from the "Top k" tokens sorted by probability.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

質問 # 14
How does the structure of vector databases differ from traditional relational databases?
  • A. It is not optimized for high-dimensional spaces.
  • B. It stores data in a linear or tabular format.
  • C. It is based on distances and similarities in a vector space.
  • D. It uses simple row-based data storage.
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation=
Vector databases store data as high-dimensional vectors (embeddings) and are optimized for similarity searches using metrics like cosine distance, unlike relational databases, which use tabular rows and columns for structured data. This makes Option D correct. Options A and C describerelational databases, not vector ones. Option B is false, as vector databases are specifically designed for high-dimensional spaces. Vector databases excel in semantic search and LLM integration.
OCI 2025 Generative AI documentation likely contrasts vector and relational databases under data storage.

質問 # 15
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
  • A. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
  • B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
  • C. T-Few fine-tuning involves updating the weights of all layers in the model.
  • D. T-Few fine-tuning requires manual annotation of input-output pairs.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.

質問 # 16
What is the function of the Generator in a text generation system?
  • A. To rank the information based on its relevance to the user's query
  • B. To generate human-like text using the information retrieved and ranked, along with the user's original query
  • C. To store the generated responses for future use
  • D. To collect user queries and convert them into database search terms
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.

質問 # 17
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 picks the most likely word at each step of decoding.
  • C. It requires a large temperature setting to ensure diverse word selection.
  • D. It selects words based on a flattened distribution over the vocabulary.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.

質問 # 18
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さまざまな年齢層の研究条件に基づくさまざまな種類のアンケートによると、当社の1Z0-1127-25テスト準備はこれらの研究グループ向けに完全に設計されており、1Z0-1127-25試験の準備時の能力と効率を向上させ、目標とする1Z0-1127-25証明書が正常に作成されました。 1Z0-1127-25の質問トレントには多くの利点がありますので、ご紹介します。Oracleの1Z0-1127-25試験に合格することができます。
1Z0-1127-25対策学習: https://www.xhs1991.com/1Z0-1127-25.html
無料でクラウドストレージから最新のXhs1991 1Z0-1127-25 PDFダンプをダウンロードする：https://drive.google.com/open?id=16idEb6QjXm9YBx0rrgCCu5-vD4ePzLa7
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