DBA AGENTIC AI FOUNDATIONS

The DBA's Guide to Oracle Agentic AI Foundations · 1Z0-1157-26

Agentic AI Cram Board

One picture that connects all six exam modules, a 60-second way to explain it, and a sticky note per module. Colours match: each box in the diagram is the same colour as its note.

40 questions60 minutes65% = 26 correct$0 on MyLearn

The whole picture

Follow a request from left to right. Every exam module sits somewhere on this path.

M1Agent basics M2Reasoning & memory M3LangChain & Agents SDK M4MCP M5OCI agents M6Oracle AI Database

Explain it in 60 seconds

If someone asks "what is agentic AI?", walk the diagram in this order.

1. A user's request reaches the agent endpoint. Sessions keep the conversation, and guardrails check what goes in and comes out.M5
2. Behind it sits the agent: an LLM with instructions, memory and a plan. It doesn't just answer; it works toward a goal.M1
3. It thinks in a loop: Thought → Action → Observation. That pattern is ReAct. Big jobs can be split across a supervisor and specialist agents.M2
4. To act, it calls tools. The LLM only asks; the application runs them. RAG for documents, SQL for tables, custom functions for APIs.M5
5. Outside tools plug in through MCP, a standard plug like an Oracle listener: client to server over JSON-RPC, exposing tools, resources and prompts.M4
6. The knowledge lives in Oracle AI Database as vectors, found by meaning with VECTOR_DISTANCE and HNSW or IVF indexes. Select AI turns English into SQL.M6
7. Developers build all of this with LangChain, LangGraph or the OpenAI Agents SDK.M3

Sticky notes

One per module. Read the "Say it" line out loud; if you can explain that sentence, you know the module.

Module 1

What is an agent?

• Agent = LLM + Instructions + Tools + Memory + Planning
• Trick: "My Instructions Tell Me Plans"
• Loop: Perceive → Reason → Act → Observe → repeat
• LLM requests a tool; the app executes it (function calling)
• Fixed steps = workflow · LLM picks steps = agent
• Human-in-the-loop → on-the-loop → autonomous
• Grounding (tools, RAG) cuts hallucination

Say itAn agent is an LLM that plans and uses tools in a loop until the goal is met.

Module 2

How agents think

• CoT: step-by-step reasoning, no tools
• ReAct: Thought → Action → Observation
• Plan-and-Execute: full plan first
• Reflection: critiques its own output
• Supervisor delegates · Handoff gives control away · Agent-as-tool keeps control
• Memory: short = context window · long = vector store
• Guardrails on input + output, least privilege, human approval, max iterations, tracing

Say itReAct is the DBA way: think, run a query, read the result, think again.

Module 3

The toolbox

• Loader → Splitter → Embeddings → Vector store → Retriever
• Prompt template · Chat model · Output parser
• LCEL: prompt | model | parser
• LangGraph: nodes, edges, state, checkpointer → loops and branches
• Agents SDK: Agent, Runner, @function_tool, Handoff, Guardrails (tripwire), Sessions, Tracing

Say itStraight line is a chain; loops and branches need LangGraph.

Module 4

MCP

• Open standard, "USB-C of AI", fixes the M×N problem
• Host holds clients; each client ↔ one server
• T-R-P: Tools = model · Resources = app (read-only) · Prompts = user
• Client offers: Sampling · Roots · Elicitation
• JSON-RPC 2.0 · stdio local · Streamable HTTP remote
• tools/list to discover, tools/call to run
• Consent, least privilege, OAuth

Say itMCP is the Oracle listener for AI: one standard way for any app to reach any tool.

Module 5

Agents on OCI

• Agent → Tools → Knowledge base → Endpoint
• Documents → RAG tool (with citations)
• Tables → SQL tool (give schema description)
• Actions → custom function tool
• KB stores: Object Storage · OpenSearch · Oracle AI DB
• New files? Rerun the ingestion job
• Endpoint: sessions, guardrails (moderation, injection, PII), citations, trace
• Access via IAM policies + compartments

Say itDocuments go to RAG, tables go to SQL, actions go to a custom tool.

Module 6

Oracle AI Database

• VECTOR(768, FLOAT32) column
• VECTOR_EMBEDDING + ONNX model in DB
• DBMS_VECTOR_CHAIN: text → chunks → embeddings
• ORDER BY VECTOR_DISTANCE(..., COSINE) FETCH FIRST 5
• HNSW = in-memory graph · IVF = partitions, scale
• Exact = 100% · APPROX ... TARGET ACCURACY = index
• RAG: load → chunk → embed → store → embed question → search → LLM → answer
• Select AI: DBMS_CLOUD_AI + AI profile; showsql shows, doesn't run

Say itThe database is the agent's memory: vectors next to tables, under the security we already manage.

Exam day

Before you click Start

• 40 questions · 60 minutes · ~90 seconds each
• Need 26 correct (65%)
• Answer easy ones first, mark hard ones, come back
• Cut two wrong options, then choose
• Match keywords: "plan first", "critic", "read-only", "in-memory"
• Never leave a question blank
• Quiet room, ID ready, other apps closed

Say itI know the picture, so I can work out any question from it.

Independent study notes for Oracle Agentic AI Foundations Associate (1Z0-1157-26). Check current exam details and service names on Oracle MyLearn.