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Oracle 1z0-1157-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 2: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 3: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 4: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 5: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 6: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
Oracle Agentic AI Foundations Associate Sample Questions:
Which OCI capability is required for serving fine-tuned or imported custom models?
- A. Dedicated AI Clusters
- B. OCI Object Storage buckets
- C. Shared On-Demand inference
- D. Free Tier compute instances
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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?
- A. The Runner performs all arithmetic internally without involving the model or tools
- B. The model calls multiply(15, 8), receives the result, then calls divide(120, 3)
- C. The SDK automatically combines all arithmetic operations into a single tool call
- D. The model calls every available arithmetic tool before answering
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In a production MCP architecture, where are tool implementations hosted?
- A. In the MCP client application that invokes the tools.
- B. Hardcoded inside the agent file using @tool decorators.
- C. Inside the LLM's training weights.
- D. On a separate MCP server.
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When is MCP most valuable?
- A. When tools are external or cloud-hosted.
- B. When defining prompts and output parsers inside a single LangChain chain.
- C. For simple local utilities like add() and multiply() .
- D. When building a single-agent app with no external dependencies.
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What does the @function_tool decorator do in the OpenAI Agents SDK?
- A. Automatically retries the function if it raises an exception during execution.
- B. Registers the function as an HTTP endpoint the agent calls via REST.
- C. Caches the function's return value to disk for faster subsequent calls.
- D. Converts a regular Python function into a tool the agent can call.
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