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Oracle 1z0-1157-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Topic 2: Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Topic 3: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Topic 4: Agent Fundamentals and Reasoning Patterns | - AI agent core concepts and architectures - Agent reasoning patterns and workflows |
| Topic 5: OCI Enterprise AI Platform | - OCI Enterprise AI Agents and Knowledge Bases - OCI Enterprise AI services overview |
| Topic 6: Enterprise Agent Development and Governance | - Function calling and tool integration - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring |
Oracle Agentic AI Foundations Associate Sample Questions:
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
- A. File Search, Code Interpreter, Function Calling, and MCP Calling.
- B. SSH, FTP, and RDP tools.
- C. SMS, Email, and Fax connectors.
- D. VCN routing and load balancing tools.
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
Which three model categories are available through OCI Enterprise AI Models?
- A. Robotics, Audio, and Vision models.
- B. Forecasting, Clustering, and Regression models.
- C. Chat models, Embed models, and Rerank models.
- D. SQL, NoSQL, and Graph models.
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
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 model calls every available arithmetic tool before answering
- 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 Runner performs all arithmetic internally without involving the model or tools
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
Why are docstrings especially important when defining LangChain tools with the @tool decorator?
- A. They encrypt tool arguments before execution.
- B. They tell the LLM when and how to use the tool.
- C. They improve Python execution speed.
- D. They determine which API provider LangChain uses.
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Why is chunking necessary before generating embeddings for large documents?
- A. To overcome token limits for large documents.
- B. To remove semantic meaning from the document.
- C. To convert text into SQL-compliant rows.
- D. To automatically encrypt content before indexing.
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