I recently completed the Oracle 1Z0-1157-26 – Agentic AI Foundations Associate exam.
I had been reading and learning about AI agents, MCP, vector search and some of the newer AI capabilities around Oracle for a while, so this certification came at a good time. I decided to go through the complete learning path rather than preparing only from an exam point of view. And I think that is the right way to approach this one.
What I liked about this course
One thing I liked is that the course does not assume that everyone already understands how an AI agent works. It starts with a fairly simple question: what actually makes something an AI agent?
From there, it introduces the role of the LLM, tools and the orchestration loop, and then moves into reasoning patterns such as Chain-of-Thought and ReAct, along with safety and guardrails. That foundation helps because the later topics make much more sense once you understand the basic agent execution flow.
The course then moves through LangChain, Model Context Protocol (MCP), OpenAI Responses API and Agents SDK, OCI Enterprise AI Agents, and finally Agentic AI capabilities around Oracle AI Database. It is not only about learning definitions. Oracle's current course also includes practical exposure to building AI agents and understanding how these pieces fit together. (Refer > Oracle MyLearn)
MCP was one of the interesting parts for me
Before going through this learning path, MCP was something I had seen mentioned more and more whenever AI agents were discussed.
The course gives a good foundation around MCP hosts, clients, servers, tools, resources and prompts, along with JSON-RPC messaging and the different transport options.
Understanding MCP also makes it easier to see why it is becoming relevant. Instead of every AI application building its own custom integration for every external tool or data source, MCP provides a standard way for these components to communicate.
For anyone starting with Agentic AI, I think this is an area worth spending some extra time on rather than just remembering the terminology for the exam.
The Oracle Database part made it more relevant
The later part of the course was particularly interesting because it brings Agentic AI closer to the database.
It covers Oracle AI Vector Search, vector embeddings, similarity search, document chunking, Select AI, Private Agent Factory and the Autonomous AI Database MCP Server.
This is where some of the concepts from the earlier modules start connecting together. You first learn what an agent is, how it calls tools and how MCP works. Then you see how similar ideas can be used with enterprise data sitting inside Oracle AI Database.
For database professionals, I think this part is especially useful because AI is no longer something completely separate from the database world. We are gradually seeing these capabilities getting closer to where enterprise data actually resides.
About the 1Z0-1157-26 exam
The exam format is quite straightforward:
Exam: 1Z0-1157-26 – Agentic AI Foundations Associate
Questions: 40
Duration: 60 minutes
Passing score: 65%
The questions are mostly foundation and concept-oriented, but I would not recommend relying only on memorizing definitions. You should understand things such as what happens during an agent loop, how tools are invoked, the difference between various MCP components, how handoffs work between agents, the purpose of guardrails, and the basic workflow behind embeddings and vector search.
Oracle's learning path also includes skill checks, exam-preparation material and a practice exam before the certification exam. The certification is currently offered free of charge. (Oracle Blogs)
How I would prepare
My suggestion would be to complete the learning material first and then use the practice exam to identify areas where the concepts are still not completely clear.
I would pay particular attention to -- > agent architecture and execution flow, LangChain tools and agents, MCP architecture, OpenAI Agents SDK concepts, OCI Enterprise AI Agents, embeddings and Oracle AI Vector Search.
For several topics, understanding why a particular component exists is more useful than simply remembering what it is called. That approach also makes the learning useful after the certification is finished.
Should you take this course ?
I would encourage anyone who is curious about Agentic AI to go through this course.
You do not necessarily need to be working as an AI or ML engineer to get value from it. If you are working with databases, cloud infrastructure, development, DevOps/SRE or OCI, it gives you a good starting point for understanding where AI agents are heading and how they can interact with existing enterprise systems.
Oracle describes the course as a practical introduction that starts from first principles and then works through LangChain, OpenAI Agents SDK, MCP, OCI Enterprise AI Agents and Oracle AI Database.
That is probably the part I liked most about it: you don't have to begin as an Agentic AI expert. Start with the fundamentals, understand how an agent works, and the rest of the pieces gradually start making sense.
My Recommendation
If Agentic AI is something you have been hearing about but haven't yet found a structured place to start, I would definitely recommend giving this learning path a try.
And I would not approach it only with the goal of clearing 1Z0-1157-26. Take some time to understand the agent loop. Build something small. Understand why tools are needed. Look at how MCP connects systems, and then see how those same ideas are being brought into OCI and Oracle AI Database.
The certification is a nice milestone at the end, but the real value is understanding how these technologies are beginning to fit together.
If you have been planning to start learning Agentic AI, this is a good course to begin with.
References
Oracle Agentic AI Foundations (2026) – Oracle MyLearn
Oracle Agentic AI Foundations course
Oracle University – Agentic AI Foundations Training & Certification
Oracle Agentic AI Foundations: Get skilled for the Agentic AI Era
Cheers..! 🫡🙌😊
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