Key details for this exam, checked against the published exam outline
Each question shows the correct answer and an explanation of why it is right
A retail company wants to quickly provision a fully managed database that automatically handles patching, tuning, backups, and scaling without requiring a DBA to configure the underlying infrastructure. They also want the flexibility to choose whether the compute and storage resources are shared with other tenants or dedicated exclusively to their organization.
Which Oracle Autonomous AI Database deployment characteristic addresses this requirement?
Oracle Autonomous AI Database provides two deployment choices: Serverless (shared infrastructure, auto-scaling) and Dedicated (private cloud infrastructure exclusive to the customer). Both are self-driving, self-securing, and self-repairing, automating patching, tuning, and backups. The other options incorrectly limit deployment options or describe manual administration, which contradicts the autonomous nature of the service.
A logistics company stores shipment records as JSON documents in an Autonomous AI Database table. They want to query specific fields within the JSON documents using standard SQL without needing to first convert the JSON into relational tables, while also being able to enforce that certain fields conform to expected data types.
Which capability of Oracle AI Database JSON support enables this?
Oracle AI Database treats JSON as a first-class data type, allowing SQL/JSON functions (such as JSON_VALUE, JSON_TABLE, JSON_EXISTS) to query JSON directly, and supports JSON Schema for validating document structure and types. There is no need for external NoSQL stores or middleware, and JSON is not limited to unstructured BLOB storage.
A telecommunications company wants to analyze relationships between customers, devices, and network towers to detect fraud rings by identifying chains of connections that traditional relational joins struggle to express efficiently. They want to run pattern-matching queries like finding all customers connected within three hops of a flagged device.
Which Oracle AI Database capability is best suited for this use case?
Graph capabilities in Oracle AI Database, including Property Graph Views, allow multi-hop relationship traversal and pattern matching (via PGQL or SQL/PGQ) that would otherwise require complex, expensive recursive joins in a purely relational model. Star schemas, materialized views, and JSON traversal are not designed for efficient variable-length graph pattern queries.
A development team is building a semantic search feature that converts product descriptions into numeric representations capturing their meaning, then stores these representations in an Oracle AI Database table so they can find similar products even when exact keywords don't match. They plan to use the VECTOR data type and an approximate nearest neighbor index for fast retrieval at scale.
Which statement accurately describes this approach?
Oracle AI Database natively supports the VECTOR data type along with distance functions (COSINE, EUCLIDEAN, DOT, MANHATTAN) and vector indexes such as HNSW (in-memory graph-based) and IVF (partition-based) for approximate nearest neighbor search, enabling fast similarity search at scale without needing an external vector database.
A business analyst with no coding experience wants to build an AI agent that can answer natural language questions about sales data stored in Autonomous AI Database, automatically retrieving relevant records and generating a summarized response using a large language model, without writing custom application code or managing infrastructure.
Which combination of Oracle capabilities is most appropriate for this scenario?
Select AI enables natural language to SQL translation and integration with LLMs directly in Autonomous AI Database, while Agent Factory provides a no-code way to build AI agents that can orchestrate retrieval (including AI Vector Search for RAG) and generation. This matches the no-code, no-infrastructure-management requirement. APEX alone doesn't train LLMs, manual PL/SQL contradicts the no-code goal, and SQL Worksheet has no built-in AI summarization.
60 questions covering all exam domains, starting from $20
Exam domains verified against: Official Oracle 1Z0-1195-26 exam guide, last checked September 2026.
Explain modern data characteristics and the Oracle AI Database 26ai converged strategy. Describe Autonomous AI Database characteristics, offerings, and deployment choices including creating Serverless instances for basic workloads.
Sample question from this domain above: Q1
Explain JSON capabilities in Oracle AI Database and distinguish when graph capabilities and Property Graph Views fit business use cases. Describe core graph concepts and graph analytic capabilities.
Describe AI, AGI, and machine learning foundations. Explain vectors, embeddings, and the Oracle VECTOR data type, and apply vector distance and indexing concepts to similarity search needs.
Sample question from this domain above: Q4
Describe Select AI in Autonomous AI Database. Apply AI Vector Search to combined semantic and business-data search scenarios and determine how it supports GenAI pipelines and RAG.
Describe Database Actions and core development tools. Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks.
Describe Oracle APEX as Oracle's low-code platform. Choose the appropriate Agent Factory capability for a no-code AI agent use case.
Sample question from this domain above: Q5
Common questions about the exam itself
