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| Section | Objectives |
|---|---|
| LangChain for AI Agents | - LangChain fundamentals
|
| Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| Introduction to MCP | - Model Context Protocol fundamentals
|
| OpenAI Responses API and Agents SDK | - OpenAI agent development
|
| Introduction to AI Agents | - AI agent fundamentals and architecture
|
| OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
Xhs1991は市場でテストされたすべての1z0-1157-26浮き沈みを経験してきましたが、1z0-1157-26試験問題は完全にプロフェッショナルになりました。Agentic AI Foundations Associate 最後に明るい光がある限り、道路で起こった困難を回避することはありません。それはあなたが望む満足のいく結果です。 知識の理論とクイズの問題の練習の両方がAgentic AI Foundations Associate、試験に対処する際にあなたがより熟練するのに役立ちます。 当社の専門家は、1z0-1157-26すべての有用なコンテンツを統合することにより、Oracle試験の重要なポイントをトレーニング資料に抽出しました。
質問 # 45
Which statement describes the OCI Responses API?
正解：A
解説：
The OCI Responses API is explicitly designed as an OpenAI-compatible API for interacting with supported models and constructing agentic workflows. Oracle documents it as both OpenAI-compatible and Open Responses-compliant, allowing applications to use familiar Responses API request structures and the OpenAI SDK while routing execution to OCI Generative AI. The uploaded examination source likewise marks B as correct.
Compatibility is important because developers can use established client patterns rather than adopting a proprietary OCI-only programming interface. Oracle specifically recommends the OpenAI SDK for calling the OCI Responses API. The OCI endpoint differs in its base URL, authentication model, available OCI- hosted models, and platform governance, but the request structure follows the compatible Responses API model.
The interface is also not restricted to a single model provider. OCI supports multiple supported hosted models.
Nor is the API read-only: agent workflows can use Function Calling and MCP Calling, allowing applications to execute external actions through controlled tool implementations. It also supports File Search and Code Interpreter.
Therefore, the defining statement among the choices is B.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - OCI Responses API, OpenAI compatibility, supported models, tools, and SDK interoperability.
質問 # 46
When is MCP most valuable?
正解：D
解説：
MCP delivers its greatest architectural value when an AI application must interact with capabilities that exist outside the application's own process , particularly external services, databases, SaaS platforms, APIs, or cloud-hosted tools. Instead of implementing a proprietary integration contract for every agent/tool combination, an MCP server exposes those capabilities using a standardized protocol.
The MCP specification describes the protocol as a standardized mechanism for integrating LLM applications with external data sources and tools . Its tools specification further states that MCP servers can expose capabilities that query databases, call APIs, perform computations, or otherwise interact with external systems.
For trivial local functions such as add() or multiply() , a normal in-process function-tool definition is usually simpler because no separate server protocol is needed. Similarly, an application that has no external dependencies gains relatively little from introducing MCP solely for prompts or local output parsers.
Thus the decisive use case is integration across system or deployment boundaries, especially where capabilities should be reusable by multiple MCP-compatible clients.
Therefore, D is the correct answer and is also explicitly marked correct in the uploaded source.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - external tools, MCP servers, interoperability, reusable integrations, and remote services.
質問 # 47
Which approaches are supported by OCI Enterprise AI Agents?
正解：C
解説：
OCI Generative AI defines two principal approaches for developing enterprise-grade agentic applications.
The first is to build agents using the OCI Responses API , an API-first model that allows developers to control agent interactions through an OpenAI-compatible interface. The second is to deploy hosted agentic applications using OCI Generative AI Applications and Deployments, where OCI manages substantial portions of the application runtime infrastructure. Oracle explicitly documents these as the two main Enterprise AI Agent approaches and notes that they can also be combined in hybrid architectures.
The Responses API approach is appropriate when developers want direct programmatic control over models, tools, context, and agent behavior without independently managing inference infrastructure. Hosted agent applications are appropriate when custom agent runtimes need managed container deployment, networking, identity, storage integration, scaling, and production lifecycle support. OCI's broader Generative AI architecture positions these mechanisms within its Enterprise AI Agents layer.
The supported architecture is therefore not divided according to Python versus Java, pricing categories, or frontend versus backend classification. Option A reproduces Oracle's documented deployment choices precisely and matches the supplied examination source.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI Responses API, Applications, Deployments, hosted agentic applications, and hybrid architectures.
質問 # 48
Which behavior is NOT a characteristic of modern LLM-based AI agents?
正解：D
解説：
Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies "Requiring every execution path to be predefined" as the behavior that is NOT characteristic of an agent.
OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state.
This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.
Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.
Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.
Therefore, D is the correct answer.
Study Guide reference/topic: Introduction to AI Agents - autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.
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
質問 # 50
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