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| Section | Objectives |
|---|---|
| OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Agent Fundamentals and Reasoning Patterns | - AI agent core concepts and architectures - Agent reasoning patterns and workflows |
| Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Enterprise Agent Development and Governance | - Guardrails, agent tracing and monitoring - Multi-agent systems and handoffs - Function calling and tool integration |
| Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
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質問 # 56
Which message format does MCP use for client-server communication?
正解：A
解説：
MCP uses JSON-RPC 2.0 as the underlying message protocol for communication between MCP clients and MCP servers. JSON-RPC provides a structured representation for requests, responses, errors, and one-way notifications while remaining independent of the underlying transport. This separation is important because the same protocol semantics can operate over STDIO or Streamable HTTP.
The MCP architecture documentation states that the data layer implements a JSON-RPC 2.0-based exchange protocol defining message structures and semantics. It also explains that the transport layer abstracts communication details, allowing the same JSON-RPC message format to operate across supported transports.
The MCP specification similarly requires messages between clients and servers to follow JSON-RPC structures, including methods, parameters, IDs for requests, and result/error structures for responses.
SOAP/XML is a different web-service protocol family; GraphQL is primarily a query language and API runtime; Protocol Buffers is a binary serialization technology. None is the MCP-defined wire-message format.
Therefore, option B is correct and agrees with the uploaded answer key.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0, requests, responses, notifications, and transport independence.
質問 # 57
Which description defines memory poisoning in AI-agent systems?
正解：D
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 # 58
Which approaches are supported by OCI Enterprise AI Agents?
正解：D
質問 # 59
What does the @function_tool decorator do in the OpenAI Agents SDK?
正解：C
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 # 60
Which OCI services are used for observability and auditing of deployed AI agents?
正解：A
解説：
OCI production AI architectures use the standard OCI observability and governance services to provide operational visibility and accountability. OCI Logging collects and centralizes service and application logs; OCI Generative AI hosted applications can expose deployment logs that open directly in OCI Logging and the Observability and Management service. OCI Monitoring supplies metrics and alarms for monitoring resource health and operational conditions. OCI Audit records calls made to supported OCI public API endpoints, providing an authoritative record of administrative and resource-management actions for investigation and compliance. Oracle's architecture guidance specifically recommends enabling OCI Logging, OCI Monitoring, and OCI Audit logs for critical AI-platform components. The services in the other options have legitimate OCI purposes, but they do not collectively represent the principal observability-and-auditing stack. Therefore, option A is the verified combination. Oracle Docs
質問 # 61
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