1Z0-1159-26 study guide · 2026

The 6 1Z0-1159-26 domains, explained

6 areas and 24 topics. Oracle does not publish weights for these areas, so each gets equal space here. Each guide explains what the area covers, how it is assessed and where candidates slip.

Start with Domain 1 See all domains
Questions
50
Time
90 minutes
Passing score
68%
Outline
1Z0-1159-26 exam topics (2026 release; section names from secondary source, unverified)

Exam blueprint

Oracle does not publish weights, so every area is drawn the same size.

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  1. 1 Explain Oracle AI Vector Search Foundations
  2. 2 Apply Vector Distance and Indexing
  3. 3 Build Retrieval-Augmented Generation (RAG) Solutions
  4. 4 Design Select AI Agent Solutions
  5. 5 Configure Agentic Interfaces and Tool Access
  6. 6 Govern Private Agent Factory Deployments

All 6 domains

24 official topics 56–74 hrs suggested study

  1. Domain 01

    Explain Oracle AI Vector Search Foundations

    This section covers the building blocks of Oracle AI Vector Search: embeddings, the native VECTOR data type and its formats, generating embeddings with in-database ONNX models or external providers, and the SQL functions that create and inspect vectors.

    Inside this domain

    • Explain vector embeddings and semantic similarity search
    • Store embeddings with the VECTOR data type
    • Generate embeddings inside and outside the database
    • Use vector SQL functions and constructors

    Watch for Keyword search finds meaning

    Topics
    4
    Questions est.
    ~5
    Study time est.
    6–8 hrs
  2. Domain 02

    Apply Vector Distance and Indexing

    This section covers how Oracle compares vectors and makes search fast: distance metrics, exact and approximate queries, HNSW and IVF vector indexes, vector pool sizing, accuracy checks and hybrid vector indexes.

    Inside this domain

    • Select vector distance metrics
    • Perform exact and approximate similarity search
    • Create and tune HNSW and IVF vector indexes
    • Manage vector memory, accuracy and hybrid indexes

    Watch for Bigger distance is better

    Topics
    4
    Questions est.
    ~14
    Study time est.
    14–18 hrs
  3. Domain 03

    Build Retrieval-Augmented Generation (RAG) Solutions

    This section covers retrieval-augmented generation inside Oracle AI Database: extracting and chunking documents, embedding and retrieving chunks, and grounding LLM answers with DBMS_VECTOR_CHAIN or Select AI with RAG.

    Inside this domain

    • Describe the RAG workflow in Oracle AI Database
    • Prepare documents with DBMS_VECTOR_CHAIN
    • Generate, store and retrieve chunk embeddings
    • Generate grounded responses with LLM providers

    Watch for RAG retrains the model

    Topics
    4
    Questions est.
    ~12
    Study time est.
    12–16 hrs
  4. Domain 04

    Design Select AI Agent Solutions

    This section covers Select AI on Autonomous AI Database and its agent framework: AI profiles, natural-language-to-SQL actions, and the tools, agents, tasks and teams of Select AI Agent.

    Inside this domain

    • Configure Select AI profiles for natural language to SQL
    • Use Select AI actions and conversations
    • Build Select AI Agent tools, agents and tasks
    • Orchestrate agent teams and the agent loop

    Watch for Rows go to the LLM for SQL

    Topics
    4
    Questions est.
    ~8
    Study time est.
    10–14 hrs
  5. Domain 05

    Configure Agentic Interfaces and Tool Access

    This section covers how agents reach Oracle AI Database through governed interfaces: Model Context Protocol basics, the managed Autonomous AI Database MCP Server, tool design and least-privilege access controls.

    Inside this domain

    • Explain the Model Context Protocol for database agents
    • Enable and use the Autonomous AI Database MCP Server
    • Expose database capabilities as agent tools
    • Secure agent access to database data

    Watch for Client defines tools

    Topics
    4
    Questions est.
    ~6
    Study time est.
    8–10 hrs
  6. Domain 06

    Govern Private Agent Factory Deployments

    This section covers Oracle AI Database Private Agent Factory: deploying the platform in your own environment, building agents visually, choosing private or hosted models, governing data and access, and monitoring agents.

    Inside this domain

    • Describe Private Agent Factory architecture and deployment
    • Build agents with Agent Builder and prebuilt agents
    • Govern data sources, models and user access
    • Monitor and operate deployed agents

    Watch for It is SaaS

    Topics
    4
    Questions est.
    ~5
    Study time est.
    6–8 hrs

Suggested study order

No official weights are published, so follow the outline order, across about 56–74 hrs of study.

  1. 1 Explain Oracle AI Vector Search Foundations 6–8 hrs
  2. 2 Apply Vector Distance and Indexing 14–18 hrs
  3. 3 Build Retrieval-Augmented Generation (RAG) Solutions 12–16 hrs
  4. 4 Design Select AI Agent Solutions 10–14 hrs
  5. 5 Configure Agentic Interfaces and Tool Access 8–10 hrs
  6. 6 Govern Private Agent Factory Deployments 6–8 hrs

Try one

Practice questionDomain 1: Explain Oracle AI Vector Search Foundations

A healthcare provider must embed patient notes stored in Oracle AI Database 26ai. Policy forbids sending note text to any service outside the database. Which approach meets the requirement?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Keyword search finds meaning

    Clue The scenario stresses different wording with the same meaning.

  • Domain 2

    Bigger distance is better

    Clue The query orders by VECTOR_DISTANCE.

  • Domain 3

    RAG retrains the model

    Clue Documents change often or answers must cite sources.

  • Domain 4

    Rows go to the LLM for SQL

    Clue The question is about data exposure during SQL generation.

  • Domain 5

    Client defines tools

    Clue The question asks where tool scope is controlled.

  • Domain 6

    It is SaaS

    Clue The scenario mentions on-premises or self-managed infrastructure.

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Study guides for exams that overlap with 1Z0-1159-26.

Practise what you read

1Z0-1159-26 domains: common questions

How many domains are in the 1Z0-1159-26 exam?

1Z0-1159-26 has 6 domains with 24 official topics between them: Explain Oracle AI Vector Search Foundations, Apply Vector Distance and Indexing, Build Retrieval-Augmented Generation (RAG) Solutions, Design Select AI Agent Solutions, Configure Agentic Interfaces and Tool Access and Govern Private Agent Factory Deployments.

What is the 1Z0-1159-26 exam format?

Questions: 50. Time allowed: 90 minutes. Passing score: 68%.

How long should I study for 1Z0-1159-26?

Our planning estimate is 56–74 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Work through the areas in order, since each builds on the one before. Use the checklist on each page to confirm you can do what it asks before moving on.

Which outline is this guide based on?

This guide follows 1Z0-1159-26 exam topics (2026 release; section names from secondary source, unverified), published by Oracle. It was last reviewed in October 2026.

Domain names, weights and topics come from Oracle's official outline (1Z0-1159-26 exam topics (2026 release; section names from secondary source, unverified)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.