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Oracle 1z0-1157-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Enterprise Agent Development and Governance | - Guardrails, agent tracing and monitoring - Function calling and tool integration - Multi-agent systems and handoffs |
| Topic 2: Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Topic 3: OCI Enterprise AI Platform | - OCI Enterprise AI Agents and Knowledge Bases - OCI Enterprise AI services overview |
| Topic 4: Oracle AI Database for Agentic AI | - Agentic AI capabilities in Oracle AI Database - Oracle AI Vector Search |
| Topic 5: Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
| Topic 6: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
Oracle Agentic AI Foundations Associate Sample Questions:
Question 1
When is MCP most valuable?
A. When tools are external or cloud-hosted.
B. When building a single-agent app with no external dependencies.
C. When defining prompts and output parsers inside a single LangChain chain.
D. For simple local utilities like add() and multiply() .
Question 2
Which OCI services are used for observability and auditing of deployed AI agents?
A. OCI Events, OCI Functions, and OCI Vault
B. OCI Streaming, OCI Queue, and OCI Email Delivery
C. OCI Logging, OCI Monitoring, and OCI Audit
D. OCI Data Catalog, OCI Search, and OCI DNS
Question 3
What does the @function_tool decorator do in the OpenAI Agents SDK?
A. Converts a regular Python function into a tool the agent can call.
B. Automatically retries the function if it raises an exception during execution.
C. Registers the function as an HTTP endpoint the agent calls via REST.
D. Caches the function's return value to disk for faster subsequent calls.
Question 4
What is the high-level workflow for Oracle AI Vector Search?
A. Create indexes - > Generate embeddings - > Store vectors - > Feed into LLM - > Search and query
B. Create indexes - > Search and query - > Feed into LLM - > Store vectors
C. Generate embeddings - > Store vectors - > Search and query - > Feed into LLM
D. Generate embeddings - > Store vectors - > Create indexes - > Search and query - > Feed into LLM
Question 5
How do On-Demand serving and Dedicated AI Clusters differ for OCI Enterprise AI Models?
A. On-Demand uses shared infrastructure while Dedicated AI Clusters provide isolated GPUs required for custom models.
B. Both options use identical infrastructure but are differentiated by pricing and branding only.
C. On-Demand supports only embedding models while Dedicated AI Clusters support generative models.
D. On-Demand doesn't use processors while Dedicated AI Clusters use isolated GPUs for all workloads.
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: A |






