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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Introduction to MCP	- Model Context Protocol fundamentals 1. Tool discovery and interoperability 2. MCP clients and servers 3. MCP concepts and architecture
Topic 2: LangChain for AI Agents	- LangChain fundamentals 1. Building agents with LangChain 2. LangChain and LangChain Expression Language 3. Agent invocation and orchestration flow 4. Tools, tool schemas, and tool execution
Topic 3: Introduction to AI Agents	- Agent development concepts 1. Multi-agent design patterns and handoffs 2. OpenAI Agents SDK guardrails 3. Function calling and tool use - AI agent fundamentals and architecture 1. Agent reasoning patterns including Chain-of-Thought and ReAct 2. Core agent components: LLMs, tools, and orchestration loops 3. Safety, guardrails, and responsible agentic workflows 4. AI agents, traditional chatbots, and rule-based systems
Topic 4: Agentic AI for Oracle AI Database	- Oracle AI Database agentic AI capabilities 1. Oracle AI Database Private Agent Factory 2. Select AI 3. Document chunking, embedding generation, and retrieval 4. Grounding agent responses with enterprise data 5. Vector data types, embeddings, and similarity search 6. Oracle AI Vector Search 7. Oracle Autonomous AI Database MCP Server
Topic 5: OpenAI Responses API and Agents SDK	- OpenAI agent development 1. Function calling and tools 2. Guardrails and tracing 3. OpenAI Agents SDK 4. Multi-agent handoffs 5. Responses API
Topic 6: OCI Enterprise AI Agents	- OCI Enterprise AI platform 1. OCI Enterprise AI Agents service 2. Deployment and scaling 3. Agent development, orchestration, and execution 4. Responses API, tools, memory, and vector stores 5. Building and running AI agents

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Oracle Agentic AI Foundations Associate 1z0-1157-26 Prüfungsfragen mit Lösungen (Q42-Q47):

42. Frage
Agent has multiply(a,b) and divide(a,b) . User: "What is 15 multiplied by 8, then divided by 3?" How does the OpenAI Agents SDK handle this?

A. The model calls multiply(15, 8) , receives the result, then calls divide(120, 3) .
B. The model calls every available arithmetic tool before answering.
C. The Runner performs all arithmetic internally without involving the model or tools.
D. The SDK automatically combines all arithmetic operations into a single tool call.

Antwort: A

Begründung:
The OpenAI Agents SDK uses an iterative agent loop for multi-step tool execution. In this scenario, the model first determines that it needs the multiply tool and generates a call with the arguments 15 and 8 . The application executes that tool and returns 120 as a tool result. The model receives the updated conversation state, recognizes that another operation remains, and subsequently requests divide(120, 3) . The resulting value is then available for the final response. The uploaded course source explicitly specifies this sequence.
OpenAI's Agents SDK documentation confirms that the Runner repeatedly calls the LLM, executes requested tools, appends their results, and runs the model again until final output is produced.
The Runner does not independently decide to calculate the arithmetic itself. Nor does the SDK automatically merge unrelated function calls into one synthetic operation. Likewise, an agent does not invoke every registered tool indiscriminately; the model selects the tools required by the current task.
Therefore, D accurately describes the sequential model/tool interaction.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential tool calls, tool results, Runner orchestration, and multi-step execution.

43. Frage
In the OpenAI Agents SDK, what is the role of the Runner?

A. It defines JSON schemas for function tools at compile time.
B. It handles authentication and rotates API keys for the agent.
C. It executes the agent loop.
D. It is the deployment platform for hosting agents in production.

Antwort: C

Begründung:
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.

44. Frage
Which statement describes the STDIO transport in MCP?

A. STDIO transport requires OAuth authentication for all requests
B. STDIO transport serializes messages as plain text rather than JSON
C. The server runs as an HTTP service intended for remote multi-client deployments
D. The host launches the server as a local subprocess communicating via stdin and stdout

Antwort: D

Begründung:
In MCP, STDIO is designed for local process-based communication. Under this transport, the host or client application launches the MCP server as a subprocess and exchanges protocol messages through the server's standard input ( stdin ) and standard output ( stdout ). The uploaded examination source identifies this behavior as the correct definition.
STDIO should be contrasted with Streamable HTTP , which is designed for independently running, network- accessible MCP servers and remote communication. STDIO is particularly suitable for local integrations where the MCP server executable can run on the same machine as the host application.
The transport mechanism does not change MCP's underlying message semantics. MCP communication still uses JSON-RPC structures; STDIO does not replace JSON with an unrelated plain-text protocol.
Authentication requirements such as OAuth are also not an inherent requirement of STDIO. OAuth and HTTP-oriented authentication concerns primarily arise in remote server architectures.
Therefore, the defining STDIO behavior is local subprocess execution coupled with stdin/stdout message exchange.
Answer C correctly captures the architecture described by MCP and by the uploaded course source.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - STDIO transport, local MCP servers, subprocess lifecycle, JSON-RPC, and Streamable HTTP comparison.

45. Frage
Which four behaviors does every Select AI Agent perform?

A. Plan, Execute, Monitor, and Scale
B. Retrieve, Rank, Generate, and Validate
C. Plan, Use tools, Summarize, and Terminate
D. Plan, Use tools, Reflect, and Remember

Antwort: D

Begründung:
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.

46. Frage
Which responsibilities are handled by OCI Enterprise AI Agents?

A. Chunking and indexing documents for retrieval-augmented generation.
B. Defining the user prompt and instructions for the database.
C. Managing hosted endpoints, runtime scaling, session management, and observability.
D. Routing requests between OCI compute instances and storage buckets.

Antwort: C

Begründung:
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.

47. Frage
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