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| Section | Objectives |
|---|---|
| OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| OpenAI Responses API and Agents SDK | - OpenAI agent development
|
| Introduction to MCP | - Model Context Protocol fundamentals
|
| Introduction to AI Agents | - Agent development concepts
|
| LangChain for AI Agents | - LangChain fundamentals
|
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NEW QUESTION # 44
In the OpenAI Agents SDK, what is the role of the Runner?
Answer: D
Explanation:
The Runner is responsible for executing the OpenAI Agents SDK agent loop. The uploaded course source identifies this directly as the Runner's role.
When Runner.run() , Runner.run_sync() , or Runner.run_streamed() is invoked, the Runner starts with an agent and user input, calls the configured model, evaluates the model output, and decides what happens next.
If the output is final, execution terminates. If the model requests a tool call, the Runner executes the tool, appends the result, and calls the model again. If the model produces a handoff, the Runner updates the active agent and continues the loop. OpenAI's official documentation describes precisely this lifecycle.
The Runner is therefore an orchestration/runtime component rather than an agent-hosting deployment service.
Authentication configuration exists separately, and function-tool JSON schemas are generated by the tool- definition mechanisms rather than being the Runner's primary responsibility.
This distinction is central to the SDK architecture: the Agent defines behavior and capabilities , while the Runner executes the iterative workflow .
Therefore, C is correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Runner, agent loop, tool execution, handoffs, final output, and runtime orchestration.
NEW QUESTION # 45
In JSON-RPC 2.0, what is the difference between a request and a notification?
Answer: A
Explanation:
The defining distinction is the presence of a request identifier and the expectation of a corresponding response. In JSON-RPC 2.0, a normal request contains an id value so that the sender can correlate the response with the request. A notification deliberately omits the ID because no response is expected. The supplied course material identifies exactly this distinction.
MCP uses JSON-RPC 2.0 as its underlying messaging protocol. Its architecture documentation explicitly states that clients and servers exchange requests and responses, while notifications are used where no response is required. MCP's notification examples contain no id field, and the documentation explains that this follows JSON-RPC notification semantics.
The difference has nothing to do with whether data is structured, whether encryption is enabled, or which transport is used. Both requests and notifications can carry structured JSON parameters. Security belongs to the transport/authentication layer, while MCP can transmit JSON-RPC messages over supported transports such as STDIO or Streamable HTTP.
Therefore, B is the precise protocol-level distinction.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0 requests, responses, IDs, and notifications.
NEW QUESTION # 46
What occurs during the MCP initialization phase?
Answer: C
Explanation:
MCP initialization establishes compatibility between the MCP client and server before normal protocol operations begin. During initialization, the parties establish a mutually supported protocol version and exchange their supported capabilities and implementation information. The client initiates the process with an initialize request containing its protocol version, capabilities, and client information. The server responds with its supported protocol version, capabilities, and server information, after which the client signals that initialization has completed. Authentication is handled at the transport or deployment security layer rather than being the defining initialization exchange. MCP initialization also does not fine-tune the LLM or execute every registered tool. This capability-negotiation process enables OCI agent applications using MCP Calling to understand which remote capabilities can safely be used. Model Context Protocol
NEW QUESTION # 47
Which OCI services are used for observability and auditing of deployed AI agents?
Answer: C
Explanation:
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
NEW QUESTION # 48
Which four behaviors does every Select AI Agent perform?
Answer: A
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 # 49
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