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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 2: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 3: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 4: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 5: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 6: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
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問題 #52
What is the high-level workflow for Oracle AI Vector Search?
答案：C
解題說明：
Official Oracle documentation supports C. Oracle describes the typical AI Vector Search workflow in five stages: generate vector embeddings from unstructured content; store those embeddings with the associated data; create vector indexes; perform semantic/vector searches using SQL; and then use the retrieved content in an LLM prompt for RAG inference.
Therefore, the technically complete sequence is:
Generate embeddings # Store vectors # Create indexes # Search and query # Feed into LLM.
This ordering reflects the operational dependency between the stages. Embeddings must exist before they can be persisted. Vector indexes are created over stored vector columns to accelerate similarity retrieval. Search then retrieves semantically relevant content, which can subsequently be incorporated into an LLM prompt for retrieval-augmented generation.
There is an important discrepancy in the uploaded question file: it marks option A as the correct answer even though A omits the documented Create indexes stage. Because the request requires verification against official Agentic AI/Oracle material, the verified answer is C , not the supplied key's A.
Study Guide reference/topic: Agentic AI for Oracle AI Database - AI Vector Search workflow, embeddings, VECTOR storage, vector indexes, similarity search, and RAG.
問題 #53
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
答案：C
解題說明：
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.
問題 #54
Which statement describes use cases for input guardrails in the OpenAI Agents SDK?
答案：C
解題說明：
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.
問題 #55
Which statement describes the OCI Responses API?
答案：D
解題說明：
The OCI Responses API is explicitly designed as an OpenAI-compatible API for interacting with supported models and constructing agentic workflows. Oracle documents it as both OpenAI-compatible and Open Responses-compliant, allowing applications to use familiar Responses API request structures and the OpenAI SDK while routing execution to OCI Generative AI. The uploaded examination source likewise marks B as correct.
Compatibility is important because developers can use established client patterns rather than adopting a proprietary OCI-only programming interface. Oracle specifically recommends the OpenAI SDK for calling the OCI Responses API. The OCI endpoint differs in its base URL, authentication model, available OCI- hosted models, and platform governance, but the request structure follows the compatible Responses API model.
The interface is also not restricted to a single model provider. OCI supports multiple supported hosted models.
Nor is the API read-only: agent workflows can use Function Calling and MCP Calling, allowing applications to execute external actions through controlled tool implementations. It also supports File Search and Code Interpreter.
Therefore, the defining statement among the choices is B.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - OCI Responses API, OpenAI compatibility, supported models, tools, and SDK interoperability.
問題 #56
Which behavior is NOT a characteristic of modern LLM-based AI agents?
答案：B
解題說明：
Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies "Requiring every execution path to be predefined" as the behavior that is NOT characteristic of an agent.
OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state.
This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.
Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.
Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.
Therefore, D is the correct answer.
Study Guide reference/topic: Introduction to AI Agents - autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.
問題 #57
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