Oracle 1Z0-1195-26 Real Exam Dumps [September 2026 Update]

Oracle AI Database 26ai is a distinct major release – not an update to Oracle 23ai – and candidates who prepared for the 23ai-based version of this credential and are now sitting for the 1Z0-1195-26 will find that some of what they studied has evolved significantly. Oracle 23ai was an Innovation Release that introduced early versions of AI Vector Search, JSON Relational Duality, and Select AI as pioneering capabilities. Oracle 26ai, announced at Oracle AI World in October 2025 and available since January 2026, is the Long-Term Support release that took those capabilities to production-grade maturity. The differences the 1Z0-1195-26 exam specifically validates include: AI Vector Search in 26ai supports hybrid search (combining vector similarity search with traditional SQL filters in a single query in ways that 23ai’s initial implementation did not fully support), Private Agent Factory is a 26ai feature that creates AI agents operating within the Oracle Database security perimeter (it was not present in early 23ai), and JSON Relational Duality in 26ai has additional view types and more complete write-through support than the 23ai preview. Candidates who know the 23ai capabilities well are well-positioned – 26ai builds on that foundation – but assuming 23ai knowledge is sufficient without studying the 26ai enhancements will produce missed questions on Private Agent Factory, hybrid vector search, and the matured Autonomous AI Database Serverless features.

The Oracle 1Z0-1195-26 (Oracle AI Database Foundations Associate 2026) is a FREE Oracle certification validating foundational knowledge of Oracle AI Database 26ai and Oracle Autonomous AI Database 26ai. Available through Oracle University with no registration fee after completing the associated learning path. 40 questions, 60 minutes, 65% passing. No prerequisites.

Cert Empire’s 1Z0-1195-26 exam questions cover all six topic areas at 26ai-specific depth – including Private Agent Factory, hybrid vector search, and JSON Relational Duality as implemented in the production-grade 26ai release.

Exam Snapshot

Oracle AI Database 26ai: The Foundation

What Oracle AI Database 26ai is: Oracle AI Database 26ai is Oracle’s current flagship database release, designed as an AI-first converged database. It is the Long-Term Support successor to Oracle 23ai and incorporates production-grade versions of all AI capabilities introduced in 23ai. The “converged database” strategy – storing relational data, JSON documents, graph structures, spatial data, and vector embeddings in the same database with unified query access – is the architectural philosophy that underpins 26ai.

26ai vs. 23ai positioning the exam tests: Oracle 23ai introduced the capabilities; Oracle 26ai productionized them. The exam tests 26ai’s specific positioning: it is the recommended production target for organizations deploying AI-powered database applications, while 23ai was an Innovation Release not intended for long-term enterprise production use.

Topic Area 1: Oracle AI Database 26ai Strategy and Autonomous AI Database Basics

The converged database strategy: Traditional approaches require separate databases for different data types – a relational database for transactional data, a graph database for relationship data, a document store for JSON, a vector database for AI embeddings. Oracle’s converged database strategy unifies all of these in a single Oracle Database instance, enabling queries that span data types without ETL pipelines between systems. The exam tests the converged strategy concept and its benefits: reduced operational complexity, unified security model, transactions spanning multiple data types.

Autonomous AI Database characteristics: The Autonomous Database automates administration tasks that traditionally required DBA expertise: patching, tuning, backups, scaling, and index management. The exam tests the Autonomous Database deployment options:

• Autonomous Database Serverless: Fully managed, scales automatically, no infrastructure to manage, billed by ECPU consumption. Appropriate for variable workloads and applications requiring elastic scaling.
• Autonomous Database on Dedicated Exadata Infrastructure (ADB-D): Dedicated Exadata hardware for a single organization, maximum isolation, suitable for strict compliance requirements.
• Autonomous Database on Exadata Cloud@Customer: Oracle-managed Autonomous Database deployed in the customer’s data center for data residency requirements.

Workload types: The exam tests Autonomous Data Warehouse (ADW – optimized for analytics and reporting workloads, columnar storage), Autonomous Transaction Processing (ATP – optimized for OLTP workloads, row-based access), and Autonomous JSON Database (AJD – optimized for document workloads, JSON-native interface).

Provisioning an Autonomous AI Database Serverless instance: The exam tests the provisioning workflow: selecting the workload type, setting ECPU count and storage, configuring network access (public endpoint or private endpoint with VCN), and enabling features (auto scaling, Oracle APEX, Data Studio).

Topic Area 2: AI Vector Search

What vectors and embeddings are: A vector embedding is a numerical representation of unstructured data (text, images, audio) in a high-dimensional space, generated by a machine learning model. Similar content produces vectors that are geometrically close in the embedding space. AI Vector Search uses this property to find content that is semantically similar to a query, even when the exact words differ.

Storing vectors in Oracle AI Database 26ai: Oracle 26ai natively stores vector embeddings as a VECTOR data type alongside relational data. The exam tests how vectors are stored (as columns in regular Oracle tables using the VECTOR data type), how embeddings are generated (using the DBMS_VECTOR package or integrated ML models), and how vector indexes are created (using CREATE INDEX … USING HNSW for Hierarchical Navigable Small World indexing or IVF for Inverted File indexing).

Distance metrics the exam tests:

• Cosine similarity: Measures the angle between two vectors – independent of magnitude. Most appropriate for text embeddings where direction matters more than scale.
• Euclidean distance: Measures the geometric distance between two points in vector space. Appropriate when magnitude differences are meaningful.
• Dot product: Used when vectors are normalized and scales matter.

Semantic search queries: The exam tests how to write a semantic search query in Oracle SQL: the VECTOR_DISTANCE() function computes the distance between a query vector and stored vectors. A query to find the five most similar documents uses ORDER BY VECTOR_DISTANCE(embedding_col, query_vector) FETCH FIRST 5 ROWS ONLY.

Hybrid search in 26ai: A key 26ai enhancement is hybrid search – combining vector similarity search with traditional SQL predicates in a single query. Instead of retrieving all semantically similar documents and then filtering in application code, a hybrid search finds documents that are both semantically similar AND match a structured condition (such as a date range, category, or status filter). The exam tests when hybrid search is appropriate (when unstructured semantic relevance and structured data filters must both apply) and the query syntax for combining VECTOR_DISTANCE() with standard SQL WHERE conditions.

Topic Area 3: JSON and Graph Capabilities

JSON in Oracle AI Database 26ai: Oracle 26ai stores JSON documents natively using the JSON data type. The exam tests JSON storage options (JSON columns in relational tables, JSON collections), JSON path expressions for querying (dot notation: data.address.city), and the JSON data guide that automatically documents the structure of JSON in a column.

JSON Relational Duality Views: JSON Relational Duality is one of Oracle 26ai’s signature capabilities. A Duality View presents relational data as JSON documents while maintaining it in normalized relational storage. Applications can read JSON documents (getting a denormalized view of related tables) and write JSON documents (Oracle handles the relational writes to the underlying normalized tables automatically). The exam tests the core concept: the same data is accessible as both relational tables and JSON documents simultaneously, with reads and writes working through either interface without duplication.

Graph databases and PGQL: Oracle 26ai’s property graph capability stores graph data (nodes representing entities, edges representing relationships) within the Oracle Database. The exam tests the Property Graph Query Language (PGQL) as Oracle’s graph query language, graph use cases (fraud detection, network analysis, recommendation engines – where relationship traversal is the primary query operation), and how graph data integrates with relational and JSON data in the converged model.

Topic Area 4: Select AI and Private Agent Factory

Select AI – natural language to SQL: Select AI enables database users to query Oracle Databases using natural language. A user asks “Show me the top 10 customers by revenue last month” and Select AI translates this into a SQL query, executes it, and returns the results. The exam tests how Select AI is configured (connecting to an LLM provider – OCI Generative AI, OpenAI, or Azure OpenAI), the prompts Select AI uses to understand the database schema, and the use cases where Select AI is appropriate (business users without SQL knowledge, rapid data exploration, report generation by non-technical staff).

Private Agent Factory in Oracle AI Database 26ai: Private Agent Factory is a 26ai capability that enables the creation of AI agents that operate within the Oracle Database’s security and privacy perimeter. Unlike generic cloud AI agents that call external APIs (potentially exposing sensitive data), Private Agent Factory agents execute within the Oracle Database – the agent’s tools are PL/SQL procedures and functions that run inside the database security model. The exam tests what Private Agent Factory enables: building AI agents that can access and operate on enterprise database data without that data leaving the organization’s security boundary. This is a 26ai-specific feature not present in Oracle 23ai’s initial release.

Agent tools and actions: Agents created through Private Agent Factory are equipped with tools – callable database operations that the agent can invoke to retrieve data, perform calculations, or execute business processes. The exam tests the tool definition pattern: a tool is a PL/SQL function or procedure with a natural language description that the LLM uses to understand when to invoke it.

Topic Area 5: Oracle APEX and Data Studio

Oracle APEX (Application Express): APEX is Oracle’s low-code development platform for building web applications on Oracle Database. Available in Autonomous AI Database at no additional cost. The exam tests APEX capabilities: creating forms and reports from database tables without writing Java or JavaScript, building dashboards for data visualization, and the Workspace and Application administration model.

Data Studio tools in Autonomous AI Database:

• Data Load: Importing data from files (CSV, JSON, Parquet), Object Storage, or external databases into Autonomous Database tables.
• Data Transforms: Defining data transformation pipelines that clean, reshape, and load data without writing code – a visual ETL-like tool within Autonomous Database.
• Data Analysis: A spreadsheet-like interface for exploring data without SQL, built on APEX.
• Business Models: Semantic models that define business metrics and KPIs for consistent reporting.

The exam tests when each Data Studio tool is appropriate and how they integrate with the Autonomous Database environment.

Topic Area 6: Oracle Machine Learning (OML)

What OML provides: Oracle Machine Learning integrates ML directly inside the Oracle Database, enabling models to be trained and scored without moving data out of the database (avoiding data movement, network latency, and security risks of external ML platforms).

OML Notebooks: A Jupyter-compatible notebook interface embedded in Autonomous Database for interactive ML development. Supports Python, R, and SQL kernels. The exam tests OML Notebooks as the development environment for data scientists working within the Oracle Database environment.

AutoML: OML AutoML automatically evaluates multiple ML algorithms, performs hyperparameter tuning, and selects the best model for a given dataset and prediction objective. The exam tests AutoML as the appropriate tool when the goal is building a predictive model quickly without manually selecting algorithms.

In-database model scoring: After training an ML model, it can be deployed and scored inside the Oracle Database using SQL. The exam tests that in-database scoring enables application developers to call predictions using standard SQL queries without deploying a separate ML serving infrastructure.

5 Study Tips for Oracle 1Z0-1195-26

• Tip 1: Study Oracle AI Database 26ai as a distinct release from 23ai. Know what 26ai adds over 23ai: production-grade Private Agent Factory, matured JSON Relational Duality with write-through support, and hybrid vector search.
• Tip 2: Study AI Vector Search at the query level – the VECTOR_DISTANCE() function syntax, the three distance metrics (cosine, Euclidean, dot product), and the hybrid search pattern combining vector search with SQL WHERE conditions.
• Tip 3: Study Private Agent Factory as a 26ai-specific feature. Know that it enables AI agents that operate within the Oracle Database security perimeter, avoiding data exposure to external services.
• Tip 4: Study JSON Relational Duality at the concept level: the same data accessible as both relational tables and JSON documents simultaneously, with writes through either interface updating the same normalized storage.
• Tip 5: Practice with Cert Empire’s 1Z0-1195-26 exam questions covering 26ai-specific capabilities including Private Agent Factory, hybrid vector search, and the Autonomous AI Database Serverless deployment model.

Best Study Resources

• Cert Empire 1Z0-1195-26 exam questions PDF and practice simulator (2026 edition).
• Oracle University: Oracle AI Database Foundations Associate 2026 official course (learn.oracle.com).
• Oracle AI Database 26ai documentation (docs.oracle.com/en/database/oracle/oracle-database/26).
• Oracle Blog: Oracle AI World October 2025 announcements (blogs.oracle.com).
• Udemy: “1Z0-1195-26: Oracle AI Database Foundations Associate” practice test courses by Naveed Academy and others.

Why Candidates Choose Cert Empire for Oracle 1Z0-1195-26 Preparation

✔ Oracle AI Database 26ai-specific questions. Our 1Z0-1195-26 questions cover 26ai capabilities – Private Agent Factory, matured hybrid vector search, production-grade JSON Relational Duality – not just 23ai features.

✔ AI Vector Search query syntax questions. We test VECTOR_DISTANCE() function usage, distance metric selection, and hybrid search query patterns.

✔ Select AI and Private Agent Factory questions. Our questions test natural language to SQL configuration and Private Agent Factory’s within-database-boundary AI agent architecture.

✔ Autonomous AI Database deployment model questions. We test Serverless vs. Dedicated vs. Cloud@Customer selection scenarios and workload type matching (ADW vs. ATP vs. AJD).

✔ Backed by a full money-back guarantee. If our exam questions do not help you pass, we refund your purchase.

FAQ’s

What is Oracle 1Z0-1195-26?

1Z0-1195-26 is the Oracle AI Database Foundations Associate 2026 certification exam, available free through Oracle University. It validates foundational knowledge of Oracle AI Database 26ai and Oracle Autonomous AI Database 26ai, including AI Vector Search, Select AI, Private Agent Factory, JSON Relational Duality, and Oracle Machine Learning.

Is this exam free?

Yes. The 1Z0-1195-26 Oracle AI Database Foundations Associate exam is available at no cost through Oracle University after completing the associated Oracle AI Database Foundations Associate 2026 learning path.

How does Oracle AI Database 26ai differ from Oracle 23ai?

Oracle 23ai was an Innovation Release that introduced AI capabilities as early previews. Oracle 26ai is the Long-Term Support release that productionized those capabilities – including a production-grade Private Agent Factory, mature hybrid vector search, and complete JSON Relational Duality with full write-through support. 26ai is the recommended production deployment target.

What is Private Agent Factory?

Private Agent Factory is an Oracle AI Database 26ai feature that enables creating AI agents that operate within the Oracle Database security perimeter. Agent tools are PL/SQL procedures that run inside the database, ensuring sensitive data never leaves the organization’s security boundary – unlike external AI agents that call APIs and potentially expose data.

Related Certifications Worth Exploring

Oracle 1Z0-1195-26 certified professionals expanding their Oracle AI credential portfolio will find our Oracle Agentic AI Foundations Associate (1Z0-1157-26) exam questions page covers the companion agentic AI credential that pairs naturally with the AI Database credential – both are free exams frequently pursued together. For those advancing into professional-level Oracle AI Database administration, our Oracle 1Z0-183 (Oracle AI Database Administration Professional) exam questions page covers AI Vector Search, multitenant architecture, database performance, backup and recovery, and advanced Oracle AI Database administration skills that build directly on the foundations established by 1Z0-1195-26.