IT業界での先駆者として、我々Pass4Testの目的はIT認定試験に参加する皆様に助けを提供することです。我々のエリートたちは目標を達成するために、昼も夜も努力してOracle試験の数年以来のデータの分析と整理に就職しています。彼らの真面目な態度があって、我々の1z0-1157-26対策を利用するお客様のほとんどは1z0-1157-26試験に合格できます。
| Section | Weight | Objectives |
|---|---|---|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
Oracleの1z0-1157-26認定試験を除いて、最近非常に人気がある試験はまたOracle、Cisco、IBM、SAPなどの様々な認定試験があります。しかし、もし1z0-1157-26認証資格を取りたいなら、Pass4Testの1z0-1157-26問題集はあなたを願望を達成させることができます。試験の受験に自信を持たないので諦めることをしないでください。Pass4Testの試験参考書を利用することを通して自分の目標を達成することができますから。1z0-1157-26認証資格を入手してから、他のIT認定試験を受験することもできます。Pass4Testの試験問題集を手にすると、どのような試験でも問題ではありません。
質問 # 45
What is the strategic theme behind agentic AI capabilities in Oracle AI Database?
正解：A
解説：
Oracle's strategic direction is to integrate AI capabilities directly into Oracle AI Database , allowing conventional relational data, vector embeddings, semantic retrieval, natural-language interfaces, and autonomous agent functionality to operate within the database platform rather than requiring a separate AI- only data tier.
Oracle AI Vector Search illustrates this strategy. The database provides a native VECTOR data type, vector- distance functions, vector indexes, and semantic similarity search alongside traditional relational data and SQL operations. This allows embeddings and enterprise business records to remain together under existing transactional, security, and governance controls.
Select AI reinforces the same architecture. Oracle documents that Select AI runs natively inside Autonomous AI Database and Oracle AI Database, while Select AI Agent provides autonomous reasoning, tools, reflection, memory, RAG, NL2SQL, PL/SQL integration, and REST interactions within the database-oriented agent framework.
Oracle is therefore extending SQL and database functionality rather than eliminating it. Nor is Oracle replacing the relational database with a vector-only system. The strategic objective is convergence: enterprise data plus native AI capabilities in one governed database environment. Option B is therefore correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - native AI integration, Select AI, Select AI Agent, AI Vector Search, and converged data architecture.
質問 # 46
What is Oracle AI Database Private Agent Factory?
正解：C
解説：
Oracle AI Database Private Agent Factory is a no-code platform for building, testing, and deploying intelligent AI agents . The uploaded question set marks D as correct, and current Oracle documentation independently confirms that definition.
Oracle describes Private Agent Factory as a platform intended for both business users and engineers. It provides an Agent Builder with visual and drag-and-drop capabilities, enabling users to construct intelligent assistants and workflows without writing conventional application code. The platform can combine pre-built agents, custom agents, reusable templates, enterprise data, LLMs, APIs, databases, and external tools.
The strategic purpose is to lower the engineering barrier for enterprise agent creation while retaining governance and integration with Oracle AI Database capabilities. Current releases include pre-built agents and workflow automation functionality for rapidly creating business-oriented agentic solutions.
It is not an embedding backup product, dedicated Kubernetes deployment manager, or physical training appliance. Those alternatives describe unrelated infrastructure or administration capabilities.
Therefore, D is directly supported by Oracle documentation.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Private Agent Factory, no-code Agent Builder, pre-built agents, custom agents, workflows, and enterprise integration.
質問 # 47
In OpenAI Agents SDK, how does the model select which tool to call?
正解：D
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 # 48
What does the @function_tool decorator do in the OpenAI Agents SDK?
正解：B
解説：
The @function_tool decorator converts an ordinary Python function into a FunctionTool that can be exposed to an agent for model-directed invocation. The Agents SDK automatically derives important tool metadata: by default, the Python function name becomes the tool name, its docstring supplies the tool description, and the function signature is converted into a JSON schema describing the expected arguments. This structured representation allows the language model to determine when the function is relevant and generate valid arguments for it. The decorator does not inherently expose the function as a REST endpoint, persist return values to disk, or guarantee automatic retries whenever execution fails. This mechanism corresponds closely to OCI Enterprise AI Agents' Function Calling model, where application-controlled functions extend an agent beyond pure model generation and enable controlled interaction with external business logic. OpenAI GitHub
質問 # 49
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
正解：B
解説：
Oracle Autonomous AI Database includes a managed MCP Server that is natively integrated with the database rather than requiring customers to deploy and operate a separate MCP infrastructure tier. Oracle states that the service eliminates the need to manage customer-side MCP server infrastructure, directly reducing deployment and operational overhead. It also integrates with database identity, authorization, governance, auditing, network controls, database roles, Virtual Private Database policies, ACLs, and private endpoints.
Architecturally, this is significant because MCP-exposed Select AI Agent tools remain close to the database security boundary. The managed multi-tenant MCP layer can expose approved tools while relying on established database governance controls. Oracle's architecture describes a Unified Security Layer, managed MCP Server, and Select AI Agent Framework working together as an integrated stack.
The capability does not remove SQL, move database execution outside the database, or require a universal MCP host. Instead, its primary advantage is minimizing additional infrastructure while preserving strong database-native control over access and operations. Thus C is technically correct and matches the uploaded source.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Autonomous AI Database MCP Server, native security integration, governance, and managed MCP infrastructure.
質問 # 50
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