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Oracle 1z0-1157-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
Oracle Agentic AI Foundations Associate Sample Questions:
Which description defines memory poisoning in AI-agent systems?
- A. Hardware instability affecting RAM reliability.
- B. A SQL injection attack against a database.
- C. Long prompts causing temporary context loss.
- D. Malicious content inserted into persistent memory stores.
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From the LLM's perspective, what is consistent between MCP-served tools and locally defined tools?
- A. MCP-served tools always return richer outputs than local tools.
- B. The LLM can only invoke MCP-served tools after explicit user approval.
- C. The LLM receives network paths and authentication credentials for MCP tools.
- D. The LLM interacts with both through the same tool-calling interface.
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Which statement describes an LLM-based AI agent?
- A. A model-based system that uses tools and orchestration.
- B. A specialized transformer architecture.
- C. A chatbot interface with additional visual components.
- D. A newly trained standalone model type.
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt
- A. It stores tool execution history.
- B. It sends the prompt to the model API.
- C. StrOutputParser()
- D. It extracts plain text from the model response.
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
What is the high-level workflow for Oracle AI Vector Search?
- A. Create indexes - > Search and query - > Feed into LLM - > Store vectors
- B. Create indexes - > Generate embeddings - > Store vectors - > Feed into LLM - > Search and query
- C. Generate embeddings - > Store vectors - > Search and query - > Feed into LLM
- D. Generate embeddings - > Store vectors - > Create indexes - > Search and query - > Feed into LLM
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