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| Section | Objectives |
|---|---|
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Oracle AI Database for Agentic AI | - Agentic AI capabilities in Oracle AI Database - Oracle AI Vector Search |
| Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
| Enterprise Agent Development and Governance | - Function calling and tool integration - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring |
| OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Building Agents with LangChain and OpenAI Agent Stack | - LangChain components and chains - OpenAI Agents SDK usage |
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質問 # 36
Which statement describes the STDIO transport in MCP?
正解：A
解説：
In MCP, STDIO is designed for local process-based communication. Under this transport, the host or client application launches the MCP server as a subprocess and exchanges protocol messages through the server's standard input ( stdin ) and standard output ( stdout ). The uploaded examination source identifies this behavior as the correct definition.
STDIO should be contrasted with Streamable HTTP , which is designed for independently running, network- accessible MCP servers and remote communication. STDIO is particularly suitable for local integrations where the MCP server executable can run on the same machine as the host application.
The transport mechanism does not change MCP's underlying message semantics. MCP communication still uses JSON-RPC structures; STDIO does not replace JSON with an unrelated plain-text protocol.
Authentication requirements such as OAuth are also not an inherent requirement of STDIO. OAuth and HTTP-oriented authentication concerns primarily arise in remote server architectures.
Therefore, the defining STDIO behavior is local subprocess execution coupled with stdin/stdout message exchange.
Answer C correctly captures the architecture described by MCP and by the uploaded course source.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - STDIO transport, local MCP servers, subprocess lifecycle, JSON-RPC, and Streamable HTTP comparison.
質問 # 37
According to the MCP architecture model, which statement describes the client-server relationship?
正解：D
解説：
MCP follows a host-client-server architecture in which the host creates an MCP client for each MCP server it connects to. The official architecture specifies that each MCP client maintains a dedicated connection with its corresponding MCP server . A host connecting to several servers therefore normally manages several MCP client instances rather than multiplexing all servers through one shared client connection. MCP also does not require clients and servers to reside on different machines. STDIO-based servers commonly execute locally, while Streamable HTTP supports remotely deployed servers. Likewise, a remote MCP server can serve many MCP clients, so a server is not permanently restricted to one client. This client-server separation is particularly relevant to OCI Enterprise AI Agents because MCP Calling enables OCI agent workflows to consume capabilities exposed by remote MCP servers through a standardized integration model.
質問 # 38
Which statement describes the purpose of the OpenAI Responses API?
正解：A
解説：
The OpenAI Responses API is an inference and agent-interaction interface. At its fundamental level, an application supplies input together with a selected model and optional instructions, tools, or other configuration; the model then produces a response containing generated output. The uploaded course material states this core purpose directly and identifies C as correct.
OpenAI's current API reference defines the Responses endpoint as creating a model response from text, image, or file inputs and returning generated text, structured JSON, tool calls, or other supported response items. The input field provides content to the model, while the response object's output array contains items generated by the model.
Although modern Responses API functionality extends beyond simple text generation-for example, built-in tools, function calling, conversation state, structured outputs, and agentic workflows-the basic abstraction remains model input followed by generated model output.
It is not a prompt-compression billing service, a local model-hosting environment, or a foundation-model training API. Those alternatives describe completely different system responsibilities.
Therefore, C accurately expresses the core purpose being tested.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Responses endpoint, model input, generated output, tools, and agentic workflows.
質問 # 39
Which Python package is installed first for a simple OCI Responses API setup?
正解：A
解説：
A basic Python implementation of the OCI Responses API uses the official OpenAI Python SDK, installed through the openai package. Oracle's Enterprise AI Agents quick-start documentation explicitly instructs developers to install it using pip install openai and further clarifies that the Responses API should be invoked using the OpenAI SDK rather than the OCI SDK.
This is possible because OCI's Responses API implements an OpenAI-compatible interface . Developers use familiar OpenAI request structures while configuring the base URL for OCI Generative AI and supplying OCI-compatible authentication. Oracle supports multiple OCI authentication approaches, including user principals, instance principals, and resource principals, while the client API retains the OpenAI-compatible programming model.
The other packages serve unrelated purposes. boto3 is the AWS SDK for Python; requests-html is an HTTP
/HTML processing library; and Django is a Python web application framework. None is the required client package for the documented OCI Responses API quick-start.
Consequently, C is the correct answer and directly matches both Oracle's implementation instructions and the answer identified in the supplied examination file.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI Responses API, OpenAI compatibility, Python SDK setup, endpoints, and OCI authentication.
質問 # 40
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 # 41
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