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| Section | Weight | Objectives |
|---|---|---|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
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質問 # 27
From the LLM's perspective, what is consistent between MCP-served tools and locally defined tools?
正解：D
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 # 28
Which prompt addition is used for zero-shot Chain-of-Thought prompting?
正解：C
解説：
Zero-shot Chain-of-Thought prompting encourages a language model to decompose a problem into intermediate reasoning steps without supplying worked examples in the prompt. The classic prompting addition associated with this technique is "Let's think step by step." The technique is "zero-shot" because the user does not provide demonstrations showing how comparable problems should be solved; instead, a short natural-language instruction encourages stepwise decomposition.
OpenAI's published prompting guidance historically illustrates this exact technique, explaining that instructing a model to reason through a sequence of steps can improve performance on tasks requiring decomposition. The OpenAI Cookbook specifically gives "Let's think step by step" as an example of an instruction used to elicit a series of reasoning steps.
Setting temperature to zero affects sampling variability rather than creating Chain-of-Thought prompting.
Uploading structured data is unrelated to the reasoning technique, and disabling retrieval tools changes the model's information-access environment rather than prompting its problem-solving structure.
Accordingly, C is the intended and technically correct answer. The uploaded source also marks this exact phrase as correct.
Study Guide reference/topic: Introduction to AI Agents - prompting strategies, reasoning decomposition, zero-shot Chain-of-Thought, and agent reasoning patterns.
質問 # 29
Which Python package is installed first for a simple OCI Responses API setup?
正解：C
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 # 30
Which responsibilities are handled by OCI Enterprise AI Agents?
正解：C
解説：
OCI Enterprise AI Agents provides the operational and orchestration capabilities required to run agentic applications at production scale. The uploaded course source identifies these responsibilities as hosted endpoints, runtime scaling, session management, and observability . Current Oracle documentation supports that architectural classification.
OCI Generative AI Applications provide a managed runtime for agent workloads and centralize configuration for scaling, storage, networking, authentication, and deployments. Active deployments expose managed endpoints, while autoscaling controls can increase or decrease replicas according to workload metrics. OCI's Responses API also provides conversation state, Conversations, memory, and related context-management facilities for stateful agent interaction. Operational visibility is supported through OCI metrics, monitoring, endpoint telemetry, tracing, and hosted application logs integrated with OCI Observability and Management.
Document chunking/indexing is a retrieval-processing responsibility rather than the complete agent-platform role. Prompt definition remains application logic, and OCI network routing is handled by underlying OCI networking services.
Therefore, C best represents the production responsibilities of the Enterprise AI Agents layer.
Study Guide reference/topic: OCI Enterprise AI Agents - managed runtime, deployments, autoscaling, endpoints, conversations, memory, monitoring, and observability.
質問 # 31
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt
正解：B
解説：
StrOutputParser is a LangChain output parser used to convert a language-model response into a standard Python string. In an LCEL pipeline such as prompt | model | StrOutputParser() , the prompt prepares the model input, the model generates an AIMessage or equivalent model output, and StrOutputParser extracts the textual content so downstream application code receives plain text.
LangChain's official documentation demonstrates this exact pattern by composing a prompt, chat model, and StrOutputParser() into a chain and then invoking the resulting runnable. The parser therefore operates after model inference ; it does not send the request to the model and does not maintain agent or tool history.
The uploaded source presents the chain fragment across separate lines and explicitly identifies "It extracts plain text from the model response" as the correct response.
This output-parsing stage is especially useful because it isolates application code from provider-specific response-object structures and provides a predictable string output from a LangChain runnable.
Study Guide reference/topic: LangChain for AI Agents - LCEL chains, Runnable composition, model output handling, and StrOutputParser.
質問 # 32
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