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| Section | Objectives |
|---|---|
| Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
| Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Enterprise Agent Development and Governance | - Function calling and tool integration - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring |
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
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質問 # 22
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 # 23
Which statement describes the STDIO transport in MCP?
正解：B
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 # 24
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 # 25
From the LLM's perspective, what is consistent between MCP-served tools and locally defined tools?
正解：A
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 # 26
Which description defines memory poisoning in AI-agent systems?
正解：B
解説：
Memory poisoning is an agent-security attack in which malicious, misleading, or attacker-controlled information is introduced into memory that the agent may reuse in future reasoning or actions. The uploaded course source defines it as malicious content inserted into persistent memory stores and identifies C as correct.
Oracle's current AI Agent Memory security guidance explains why persistent memory must be treated as a security-sensitive surface. Model-derived memories, summaries, context cards, metadata, and retrieved records can become persistent state and later be inserted into prompts. Oracle therefore advises treating memory-derived content as untrusted and emphasizes that write-capable memory paths can influence future prompts and retrieval results.
The broader agent-security definition is also explicit in OWASP's Agentic AI guidance: memory poisoning involves malicious data being persisted in agent memory so that it can influence future sessions or behaviors.
This differs from temporary context-window pressure, SQL injection, or physical RAM corruption. The essential security property is persistence : compromised memory can affect later reasoning long after the original malicious interaction.
Therefore, C is correct.
Study Guide reference/topic: Introduction to AI Agents - agent memory, persistent state, memory poisoning, prompt injection persistence, and agent security.
質問 # 27
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