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Agentic AI for Oracle AI Database Professional (1Z0-1159-26) Syllabus

Explain Oracle AI Vector Search Foundations

Explaining vector embeddings, the VECTOR data type and how AI Vector Search works in Oracle AI Database.

10%
Weight
5
Questions
10
Marks

Vectors and Embeddings

  • Vector embeddings and similarity concepts
  • VECTOR data type and dimension formats
  • Generating embeddings in the database with ONNX models
  • External embedding providers

AI Vector Search Architecture

  • Storing vectors alongside relational data
  • Exact vs. approximate similarity search
  • Vector memory pool
  • Use cases for AI Vector Search

Apply Vector Distance and Indexing

Choosing distance metrics and vector indexes and writing similarity queries.

29%
Weight
14
Questions
29
Marks

Distance Metrics and Queries

  • VECTOR_DISTANCE function
  • Cosine, Euclidean, dot product and Manhattan metrics
  • Shorthand distance operators
  • FETCH APPROX similarity queries

Vector Indexes

  • HNSW in-memory neighbor graph indexes
  • IVF neighbor partition indexes
  • Target accuracy settings
  • Index sizing, memory and maintenance

Hybrid and Multi-Vector Search

  • Combining vector and relational filters
  • Hybrid vector indexes with text search
  • Multi-vector similarity search
  • Query plan and accuracy checks

Build Retrieval-Augmented Generation (RAG) Solutions

Building RAG pipelines in the database from document loading to LLM response.

24%
Weight
12
Questions
24
Marks

Document Processing

  • DBMS_VECTOR_CHAIN utilities
  • Converting documents to text
  • Chunking strategies and parameters
  • Embedding chunks in the database

Retrieval and Generation

  • Retrieving top-k chunks
  • Prompt augmentation with retrieved context
  • Calling LLMs from the database
  • Select AI with RAG and vector indexes

Design Select AI Agent Solutions

Designing in-database agents with Select AI Agent.

16%
Weight
8
Questions
16
Marks

Select AI Fundamentals

  • Select AI natural-language-to-SQL
  • AI profiles and LLM provider credentials
  • Select AI actions: runsql, showsql, narrate, chat
  • Metadata and comments for accuracy

Building Agents

  • DBMS_CLOUD_AI_AGENT package
  • Defining agents, tasks and teams
  • Built-in and custom tools
  • Running and monitoring agent teams

Configure Agentic Interfaces and Tool Access

Exposing database capabilities to external agents through standard interfaces.

12%
Weight
6
Questions
12
Marks

MCP and External Agents

  • Autonomous Database MCP Server
  • Registering database tools for agents
  • Connecting agent frameworks to Oracle AI Database
  • Python and LangChain integrations

Secure Tool Access

  • Least-privilege database users for agents
  • Network ACLs and credentials
  • Auditing agent tool calls
  • Limiting data exposure

Govern Private Agent Factory Deployments

Deploying and governing agents with Oracle's Private Agent Factory.

9%
Weight
5
Questions
9
Marks

Private Agent Factory

  • Private Agent Factory purpose and architecture
  • Building agents without code
  • Deploying agents on private infrastructure
  • Data residency considerations

Governance and Security

  • Access control for agents
  • Monitoring agent activity
  • Responsible AI and guardrails
  • Lifecycle management of agents

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I need help understanding Explain Oracle AI Vector Search Foundations

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Learning Progress

Explain Oracle AI Vector Search Foundations 85%
Apply Vector Distance and Indexing 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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