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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 |
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Building Agents with LangChain and OpenAI Agent Stack | - LangChain components and chains - OpenAI Agents SDK usage |
| 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 |
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질문 # 54
In the OpenAI Agents SDK, when are input guardrails and output guardrails evaluated?
정답：A
설명：
The Agents SDK separates validation at the input and output boundaries of an agent workflow. Input guardrails evaluate the initial user input, while output guardrails evaluate the final agent output before that result is accepted and returned. This makes B the intended architectural answer. A technical nuance is that current SDK input guardrails support both blocking and parallel execution: with blocking execution, validation completes before agent execution starts; with the default parallel mode, the guardrail can execute concurrently with the agent. Output guardrails, however, operate on the completed final output and always execute after the agent finishes producing it. Guardrails are runtime controls rather than decisions the LLM must explicitly request. OCI's agentic architecture similarly emphasizes governed model-and-tool workflows, making these validation boundaries important when implementing production AI agents. OpenAI GitHub
질문 # 55
Which statement describes an LLM-based AI agent?
정답：A
설명：
An LLM-based AI agent is not simply a foundation model or chatbot user interface. It is a system in which an LLM acts as a reasoning and decision-making component while an orchestration layer gives it access to instructions, tools, state, external information, and potentially other agents. This architecture enables the system to decide which actions to perform and in what sequence to pursue a defined objective.
OpenAI describes an agent as an LLM equipped with instructions, tools, and handoffs, allowing it to plan, use tools to gather information or take actions, and delegate tasks when appropriate. Oracle similarly explains that AI agents use tools to communicate with external systems and dynamically determine which tools or integrations to use and in which order to achieve a goal.
An agent therefore does not require training an entirely new model architecture. The underlying LLM may be an existing pretrained model. What makes the system agentic is the combination of model reasoning with orchestration, tool execution, observations, state, and iterative decision-making.
Accordingly, D provides the correct architectural definition and matches the answer supplied in the uploaded source.
Study Guide reference/topic: Introduction to AI Agents - LLM-based agents, reasoning, tools, orchestration, actions, observations, and agent loops.
질문 # 56
Which statement describes use cases for input guardrails in the OpenAI Agents SDK?
정답：D
설명：
Input guardrails are checks applied to the initial user input before or alongside execution of the primary agent workflow. Their purpose is to validate whether incoming content satisfies defined security, safety, relevance, or policy requirements and to interrupt execution when an unacceptable condition is detected.
The official OpenAI Agents SDK documentation states that input guardrails receive the same initial input supplied to the agent and can trigger a tripwire that stops execution. Guardrails can therefore be used to detect malicious or otherwise disallowed user requests before they propagate through an expensive or action-capable agent workflow. Blocking execution is particularly important for security-sensitive cases because it can prevent the agent from consuming tokens or executing tools when the input fails validation.
Option B describes output guardrails , which run against the final agent output. Role-based tool authorization is a separate tool-access control problem, while audit logging is normally implemented through observability, tracing, or application-level compliance mechanisms rather than defining the primary input-guardrail function.
Therefore, D is the correct answer and agrees with the supplied course source.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - input guardrails, output guardrails, tripwires, safety validation, and execution blocking.
질문 # 57
What is the default distance metric for VECTOR_DISTANCE in Oracle for non-BINARY vectors?
정답：B
설명：
Oracle AI Vector Search defines VECTOR_DISTANCE as the primary SQL function for calculating the distance between two vectors. When the function is called without explicitly specifying a distance metric, Oracle specifies COSINE as the default metric for ordinary, non-BINARY vectors. Cosine distance measures the angular relationship between vector representations and is widely used for semantic similarity because embeddings with similar meaning tend to point in similar directions in vector space. Oracle treats BINARY vectors differently: their default metric is HAMMING. Euclidean, or L2, distance is supported but must be selected when required; it is not the general default. Levenshtein distance applies to string-edit comparisons, while bitwise XOR is not the default Oracle vector-distance metric. Therefore, for the scenario stated in the question, option C is the verified answer. Oracle Docs
질문 # 58
Which tasks is handled automatically by LangChain when using agent.invoke()?
정답：B
설명：
When a LangChain agent is invoked through agent.invoke() , the agent runtime abstracts the core mechanics required for model-mediated tool execution. LangChain tools expose structured inputs and outputs, and tool definitions provide the model with schemas derived from Python type information or explicitly supplied schemas. The framework then coordinates model responses containing tool calls, dispatches the corresponding tools, passes observations back to the model, and repeats the process until the agent reaches a termination condition.
This is a major distinction between directly binding tools to a standalone chat model and using a LangChain agent. LangChain documentation states that, with a model alone, the developer must execute returned tool calls and feed results back manually; when an agent is used, the agent loop handles that tool-execution loop .
Thus, the course's intended abstraction is captured by B: building/exposing tool schemas, processing model tool calls, and orchestrating iterative execution. GPU memory distribution and model training are infrastructure/model-development responsibilities, not functions of agent.invoke() .
The supplied question source explicitly marks B as the expected answer.
Study Guide reference/topic: LangChain for AI Agents - agent.invoke(), tool schemas, tool-call parsing, ToolNode execution, and iterative agent loops.
질문 # 59
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