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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 2: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 3: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 4: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 5: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 6: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
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NEW QUESTION # 39
Which MCP primitive is model-controlled and used to perform actions?
Answer: B
Explanation:
In MCP, Tools are the primitive explicitly designed to be model-controlled. They represent executable functions that an MCP server exposes so that a language model can take actions, retrieve information, query databases, invoke APIs, modify files, or perform computations. The uploaded question set identifies Tools as the correct answer.
The official MCP specification defines three principal server primitives with different control models:
Prompts are user-controlled , Resources are application-controlled , and Tools are model-controlled . Tools can be discovered by the model-facing application and invoked automatically according to the model's contextual interpretation of the user's request.
Resources differ because they primarily provide contextual data such as file contents or database schemas.
Prompts provide reusable templates or instructions normally selected through user interaction. "Schemas" are not one of the three MCP primitives in this control hierarchy; schemas describe structures such as tool parameters rather than constituting a standalone primitive.
Therefore, A is correct.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP primitives, Tools, Resources, Prompts, control hierarchy, and tool invocation.
NEW QUESTION # 40
Which four behaviors does every Select AI Agent perform?
Answer: D
Explanation:
Oracle Select AI Agent is architected around four foundational behaviors: Planning, Tool Use, Reflection, and Memory Management . Oracle documentation describes these as the framework's principal layers. Planning interprets the user's objective, decomposes it into ordered actions, and identifies appropriate capabilities. Tool Use invokes mechanisms such as NL2SQL, RAG, PL/SQL procedures, or external REST services. Reflection evaluates observations returned by those tools and determines whether the current plan should continue, be revised, or use another capability. Memory preserves context and useful information, supporting coherent multi-turn interactions and longer-term continuity.
Oracle explicitly states that Select AI Agent combines planning, tool use, reflection, and memory and implements a ReAct-style agentic pattern in which the agent reasons, acts through tools, evaluates observations, and continues toward the goal.
The alternative answer sets describe generic information-retrieval or operational lifecycle stages but do not correspond to Oracle's defined Select AI Agent architecture. Consequently, B reproduces the four documented agent behaviors and is the correct answer in the supplied question set.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Select AI Agent architecture, Planning, Tool Use, Reflection, Memory, and ReAct.
NEW QUESTION # 41
Which authentication approach should be used for production-grade access to OCI Enterprise AI services?
Answer: C
Explanation:
For production OCI Enterprise AI workloads, Oracle recommends OCI IAM-based authentication rather than long-lived development credentials. The uploaded source identifies OCI IAM authentication with signed requests and IAM policies as the correct production architecture.
Oracle's OCI Responses API authentication documentation distinguishes service API keys used for testing and early development from IAM authentication intended for production and OCI-managed environments.
IAM-based authentication uses OCI identity principals and request-signing mechanisms and allows authorization to be controlled through centralized IAM policies. Oracle specifically recommends IAM when applications execute in services such as OCI Functions or Oracle Kubernetes Engine, when long-lived API keys should be avoided, or when fine-grained centralized access control is required.
IAM policies also implement least privilege by defining exactly which users, groups, resource principals, or workloads can access individual Generative AI resource types.
Browser cookies, anonymous tenancy access, and credentials committed into source repositories violate standard enterprise security practices and significantly increase credential-exposure risk.
Therefore, A is the only production-grade authentication approach among the choices.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI IAM, signed requests, policies, principals, least privilege, and production authentication.
NEW QUESTION # 42
What is Oracle AI Database Private Agent Factory?
Answer: C
Explanation:
Oracle AI Database Private Agent Factory is a no-code platform for building, testing, and deploying intelligent AI agents . The uploaded question set marks D as correct, and current Oracle documentation independently confirms that definition.
Oracle describes Private Agent Factory as a platform intended for both business users and engineers. It provides an Agent Builder with visual and drag-and-drop capabilities, enabling users to construct intelligent assistants and workflows without writing conventional application code. The platform can combine pre-built agents, custom agents, reusable templates, enterprise data, LLMs, APIs, databases, and external tools.
The strategic purpose is to lower the engineering barrier for enterprise agent creation while retaining governance and integration with Oracle AI Database capabilities. Current releases include pre-built agents and workflow automation functionality for rapidly creating business-oriented agentic solutions.
It is not an embedding backup product, dedicated Kubernetes deployment manager, or physical training appliance. Those alternatives describe unrelated infrastructure or administration capabilities.
Therefore, D is directly supported by Oracle documentation.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Private Agent Factory, no-code Agent Builder, pre-built agents, custom agents, workflows, and enterprise integration.
NEW QUESTION # 43
Assume an agent has access to the tools multiply(a, b) and divide(a, b). A user asks: "What is 15 multiplied by
8, then divided by 3?" In the OpenAI Agents SDK, how does the agent loop handle this multi-step task?
Answer: C
Explanation:
The OpenAI Agents SDK implements an iterative agent loop in which the model determines which available capability should be invoked, receives the resulting observation, and can then make another tool call based on that updated context. Consequently, the model first requests multiply(15, 8) . The function executes and returns 120 ; that tool output is supplied back to the model. The model then determines that the remaining operation requires divide(120, 3) and requests the second tool.
OpenAI describes Agents as LLMs equipped with tools and explains that the SDK runtime manages repeated model/tool interactions until the workflow produces final output. Function tools expose schemas and executable implementations to this orchestration process.
The Runner is responsible for coordinating the loop; it does not independently substitute its own arithmetic logic for the model's tool decisions. Similarly, the SDK does not synthesize a new combined function when two distinct tools are required, nor does it invoke every available tool without reason.
The uploaded question source explicitly marks the sequential multiply-then-divide behavior as correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential function calling, tool observations, and Runner orchestration.
NEW QUESTION # 44
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