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| Section | Objectives |
|---|---|
| Topic 1: OpenAI Responses API and Agents SDK | - OpenAI agent development
|
| Topic 2: OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Topic 3: Introduction to MCP | - Model Context Protocol fundamentals
|
| Topic 4: LangChain for AI Agents | - LangChain fundamentals
|
| Topic 5: Introduction to AI Agents | - AI agent fundamentals and architecture
|
| Topic 6: Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
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質問 # 22
Compared with a standalone LLM call, an AI agent architecture commonly adds which capabilities?
正解：D
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 # 23
From the LLM's perspective, what is consistent between MCP-served tools and locally defined tools?
正解：D
解説：
MCP standardizes how external systems expose capabilities to an AI application, but the model does not need to reason about the transport or deployment location of each capability. Once an MCP server's tools are discovered and incorporated into an agent's available tool set, they are represented to the model as callable tools with names, descriptions, and input schemas. Locally implemented function tools are presented through essentially the same model-facing tool abstraction. OpenAI's Agents SDK documentation explicitly states that tools obtained from configured MCP servers are added to the agent's list of available tools, alongside ordinary tools. Therefore, from the LLM's perspective, both are selected and invoked through the tool-calling mechanism rather than through separate network-specific interfaces.
Authentication, network connectivity, server lifecycle, authorization, and actual execution remain responsibilities of the application/MCP infrastructure. They are deliberately abstracted away from the model.
Therefore, option B precisely captures the architectural consistency described in the course question.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP tools, tool discovery, agent tool abstraction, and client-server integration.
質問 # 24
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt
正解：A
解説：
StrOutputParser is a LangChain output parser used to convert a language-model response into a standard Python string. In an LCEL pipeline such as prompt | model | StrOutputParser() , the prompt prepares the model input, the model generates an AIMessage or equivalent model output, and StrOutputParser extracts the textual content so downstream application code receives plain text.
LangChain's official documentation demonstrates this exact pattern by composing a prompt, chat model, and StrOutputParser() into a chain and then invoking the resulting runnable. The parser therefore operates after model inference ; it does not send the request to the model and does not maintain agent or tool history.
The uploaded source presents the chain fragment across separate lines and explicitly identifies "It extracts plain text from the model response" as the correct response.
This output-parsing stage is especially useful because it isolates application code from provider-specific response-object structures and provides a predictable string output from a LangChain runnable.
Study Guide reference/topic: LangChain for AI Agents - LCEL chains, Runnable composition, model output handling, and StrOutputParser.
質問 # 25
What is Oracle AI Database Private Agent Factory?
正解：B
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 # 26
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
正解：A
解説：
OCI Enterprise AI Agents supports a defined set of OpenAI-compatible agent tools through the OCI Responses API. Oracle's current documentation identifies File Search, Code Interpreter, Function Calling, and MCP Calling as supported tool categories.
File Search allows an agent to retrieve relevant information from indexed content and vector stores. Code Interpreter provides a controlled environment for computational or programmatic analysis. Function Calling lets the model request execution of application-defined functions with structured parameters. MCP Calling enables the agent to discover and invoke capabilities made available by remote Model Context Protocol servers. Together, these mechanisms allow an LLM to move beyond text generation and perform retrieval, computation, application actions, and standardized external-system integration.
Oracle additionally provides supporting agent resources such as Files, Vector Stores, Containers, Conversations, Projects, and memory capabilities, while SQL Search/NL2SQL is available as an OCI-native agent capability.
SSH, FTP, RDP, VCN routing, load balancing, SMS, and fax are not the four built-in tool categories identified in the OCI Enterprise AI Agents curriculum. Consequently, A is correct and agrees with the uploaded question set.
Study Guide reference/topic: OCI Enterprise AI Agents - File Search, Code Interpreter, Function Calling, MCP Calling, Vector Stores, and agent tools.
質問 # 27
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私たちに知られているように、Agentic AI Foundations Associate高い合格率は、高品質のTopexamの1z0-1157-26研究急流を反映しています。 試験に合格した98パーセント以上があり、これらの人々は両方ともOracleの1z0-1157-26テストトレントを使用しました。 当社の1z0-1157-26ガイド急流が他の学習教材より高い合格率を持っていることは間違いありません。 高いパスレートがすべての人々にとって非常に重要であることを深く知っているため、常にパスレートを改善するために最善を尽くしています。 現在、合格率は99％に達しました。 学習ツールとして1z0-1157-26学習トレントを選択し、慎重に学習した場合、
1z0-1157-26合格率書籍: https://www.topexam.jp/1z0-1157-26_shiken.html