近年、IT領域で競争がますます激しくなります。IT認証は同業種の欠くことができないものになりました。あなたはキャリアで良い昇進のチャンスを持ちたいのなら、JpshikenのOracleの1z0-1157-26「Agentic AI Foundations Associate」試験トレーニング資料を利用してOracleの認証の証明書を取ることは良い方法です。現在、Oracleの1z0-1157-26認定試験に受かりたいIT専門人員がたくさんいます。Jpshikenの試験トレーニング資料はOracleの1z0-1157-26認定試験の１００パーセントの合格率を保証します。
| Section | Objectives |
|---|---|
| Topic 1: OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Topic 2: OpenAI Responses API and Agents SDK | - OpenAI agent development
|
| Topic 3: LangChain for AI Agents | - LangChain fundamentals
|
| Topic 4: Introduction to AI Agents | - Agent development concepts
|
| Topic 5: Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| Topic 6: Introduction to MCP | - Model Context Protocol fundamentals
|
1z0-1157-26練習問題のソフトテストエンジンに興味がある場合は、以下の情報をよく知っておく必要があります。 ソフトテストエンジンは、最初にオンラインでパーソナルコンピューターにダウンロードしてからインストールする必要があります。 割賦後、オフラインで1z0-1157-26練習問題を使用できます。 電話、iPadなどの他の電子製品にコピーすることもできます。 一方、Agentic AI Foundations Associate試験問題は200台以上のパソコンで使用できます。 あなたの会社の1z0-1157-26練習問題のソフトテストエンジンを購入すると、非常に便利です。
質問 # 30
In the OpenAI Agents SDK, what is the role of the Runner?
正解：C
解説：
The Runner is responsible for executing the OpenAI Agents SDK agent loop. The uploaded course source identifies this directly as the Runner's role.
When Runner.run() , Runner.run_sync() , or Runner.run_streamed() is invoked, the Runner starts with an agent and user input, calls the configured model, evaluates the model output, and decides what happens next.
If the output is final, execution terminates. If the model requests a tool call, the Runner executes the tool, appends the result, and calls the model again. If the model produces a handoff, the Runner updates the active agent and continues the loop. OpenAI's official documentation describes precisely this lifecycle.
The Runner is therefore an orchestration/runtime component rather than an agent-hosting deployment service.
Authentication configuration exists separately, and function-tool JSON schemas are generated by the tool- definition mechanisms rather than being the Runner's primary responsibility.
This distinction is central to the SDK architecture: the Agent defines behavior and capabilities , while the Runner executes the iterative workflow .
Therefore, C is correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Runner, agent loop, tool execution, handoffs, final output, and runtime orchestration.
質問 # 31
What is the high-level workflow for Oracle AI Vector Search?
正解：D
解説：
Official Oracle documentation supports C. Oracle describes the typical AI Vector Search workflow in five stages: generate vector embeddings from unstructured content; store those embeddings with the associated data; create vector indexes; perform semantic/vector searches using SQL; and then use the retrieved content in an LLM prompt for RAG inference.
Therefore, the technically complete sequence is:
Generate embeddings # Store vectors # Create indexes # Search and query # Feed into LLM.
This ordering reflects the operational dependency between the stages. Embeddings must exist before they can be persisted. Vector indexes are created over stored vector columns to accelerate similarity retrieval. Search then retrieves semantically relevant content, which can subsequently be incorporated into an LLM prompt for retrieval-augmented generation.
There is an important discrepancy in the uploaded question file: it marks option A as the correct answer even though A omits the documented Create indexes stage. Because the request requires verification against official Agentic AI/Oracle material, the verified answer is C , not the supplied key's A.
Study Guide reference/topic: Agentic AI for Oracle AI Database - AI Vector Search workflow, embeddings, VECTOR storage, vector indexes, similarity search, and RAG.
質問 # 32
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
正解：D
質問 # 33
In OpenAI Agents SDK, how does the model select which tool to call?
正解：C
解説：
Tool selection in the OpenAI Agents SDK is model-driven. Each function tool exposes structured metadata that gives the model enough information to determine whether the tool is appropriate and how it should be invoked. The SDK represents a function tool using a name , description , and JSON parameter schema .
OpenAI's SDK reference explicitly defines these properties as information shown to the LLM, while function- tool helpers automatically generate the parameter schema from the Python function signature and derive descriptions from documentation when available.
During an agent run, the model evaluates the user's request together with the available tool definitions. It can then select an appropriate tool and generate arguments conforming to that tool's schema. This mechanism is fundamentally semantic and contextual: meaningful names and descriptions tell the model what a tool does, while schemas describe the arguments required to execute it.
There is no rule requiring the first registered tool to be selected, every tool to be invoked, or random selection.
Such behavior would undermine agentic reasoning and dynamic orchestration. Consequently, B is the technically correct answer and is explicitly identified as correct in the uploaded question set.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Function Tools, tool metadata, JSON schemas, tool selection, and model-driven invocation.
質問 # 34
Compared with a standalone LLM call, an AI agent architecture commonly adds which capabilities?
正解：C
解説：
A standalone LLM request typically consists of supplying input and receiving model-generated output. An AI agent adds an orchestration layer that enables the model to participate in a broader execution loop. Oracle's Enterprise AI Agents architecture explicitly combines model interaction with tools, memory, conversation state, reasoning, and multi-step orchestration . Tools allow an agent to retrieve information or perform actions through File Search, Function Calling, Code Interpreter, or MCP Calling. Memory preserves relevant state within or across conversations, while iterative execution enables the agent to evaluate intermediate results and determine subsequent actions until the task is complete. These capabilities do not require changing the transformer's architecture, increasing its training speed, or providing native graphical-interface rendering.
Therefore, tool access, memory handling, and iterative execution are the defining additions described by option A. Oracle Docs
質問 # 35
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1z0-1157-26テストの質問で提供されるサービスは、非常に具体的かつ包括的なものです。まず第一に、私たちのテスト材料は多くの専門家から来ています。材料の金含有量は非常に高く、更新速度は高速です。 1z0-1157-26試験準備では、学習ニーズに応じていつでも最適な情報を見つけて、いつでも調整して完成させることができます。 1z0-1157-26学習教材は、情報を提供するだけでなく、学習とレビューのスケジュールに従って、1z0-1157-26学習ガイドはお客様に合わせてカスタマイズされています。
1z0-1157-26独学書籍: https://www.jpshiken.com/1z0-1157-26_shiken.html