Oracle 1Z0-931-26 Real Exam Questions [October 2026 Update]

The naming shift from “Autonomous Database Cloud Professional” to “Autonomous AI Database Professional” signals precisely what changed in the 2026 exam – and candidates who prepare from prior-year study materials without updating for the AI domains will lose the points that separate a pass from a fail. The AI features are not elective enrichment on this exam. Select AI and AI Vector Search collectively represent nearly a third of the exam questions at 2026 weights, and they test at implementation depth, not marketing-brochure awareness. Select AI is Oracle’s natural language to SQL capability – a user asks a question in plain English against their Autonomous Database, and the system generates and executes the appropriate SQL. The exam tests Select AI at the operational level: the SELECT AI action syntax (SELECT AI <text>) the five specific actions available (narrate, runsql, showsql, explainsql, chat), how Select AI connects to an AI provider (the AI profile, which specifies the LLM and the credential), and what credentials are required before a SELECT AI statement can execute. Candidates who know that Select AI “allows natural language queries” but cannot distinguish narrate from explainsql will fail the Select AI questions.

The Dedicated Exadata Infrastructure provisioning hierarchy is the second area where 2026 candidates routinely underperform – not because it is obscure but because the four-layer structure has no equivalent in any other Oracle product, and candidates study it as a list rather than as a dependency chain with operational implications. The layers are: Autonomous Exadata Infrastructure (the physical Exadata rack in OCI) → Autonomous VM Cluster (the logical compute cluster on the rack) → Autonomous Container Database (the Oracle Database container that provides isolation between tenants sharing the cluster) → Autonomous Database (the individual ADB instance). Moving up this hierarchy to change a lower layer – for example, scaling the Exadata Infrastructure – does not automatically propagate to the layers above it. The exam tests this hierarchy as a provisioning and management workflow: what is created in what order, what parameters are set at which layer, and what the DBA must do at each layer to achieve a described operational outcome.

The Autonomous Recovery Service deserves specific attention in the context of this exam. Many candidates know Autonomous Database’s self-backup capability but do not specifically know that the Autonomous Recovery Service – Oracle’s managed backup and recovery service separate from the database’s built-in backup – provides continuous data protection for Autonomous Database with a guaranteed RPO in the order of seconds rather than the 24-hour RPO of daily scheduled backups. The exam tests the Autonomous Recovery Service as a distinct backup destination option with its own RPO characteristics, not as the same thing as the automatic backup that Autonomous Database performs to Object Storage.

Exam Snapshot

Domain 1: Autonomous Database Fundamentals

Autonomous Database offerings: Oracle’s Autonomous Database comes in two workload types and two deployment models.

Workload types: Autonomous Transaction Processing (ATP – optimised for OLTP workloads: high-concurrency, low-latency transactional queries) and Autonomous Data Warehouse (ADW – optimised for analytical workloads: large scans, aggregations, columnar query patterns). The same underlying Autonomous Database platform powers both; the workload type setting configures how the database self-tunes.

Deployment models: Serverless (shared Exadata infrastructure managed entirely by Oracle – customers provision only the database, Oracle manages everything else) and Dedicated (customer-dedicated Exadata infrastructure – the four-layer hierarchy described above).

ECPU billing model: Autonomous Database uses ECPU (Elastic Compute Processing Unit) as its compute unit – a fractional CPU allocation that scales more granularly than the older OCPU model. ECPU auto-scaling allows the database to use up to three times the provisioned ECPUs during peak demand, billing only for the additional ECPUs consumed. The exam tests ECPU scaling as a serverless-specific capability – the database scales within minutes in response to workload demand.

Database Actions: Database Actions is the integrated web-based tool suite available from the Autonomous Database console – providing SQL Worksheet (for running queries and scripts), Data Load (for loading data from files, Object Storage, or external databases), Data Transforms (for ETL pipeline building), APEX (Oracle Application Express – the low-code development platform), Oracle Machine Learning Notebooks, and the REST API management interface. The exam tests Database Actions as the primary development and management interface for Autonomous Database, distinct from traditional Oracle tools like SQL*Plus or SQL Developer.

Autonomous Database lifecycle operations: Creating a clone (a full copy of the database for testing or development), creating a refreshable clone (a read-only clone that periodically refreshes from the source – keeping the test environment current without manual data copying), stopping and starting an ADB instance (available only in Serverless – the instance is stopped and billing for ECPUs ceases while storage billing continues), and moving a database (changing its compartment or moving between regions).

Domain 2: Serverless and Dedicated Deployment

Serverless Autonomous Database is the fully managed model – Oracle manages the Exadata infrastructure, the network, the patching, and the backups. The DBA manages the database itself: user accounts, schemas, performance, and data. Serverless auto-patches – the database receives quarterly Oracle Database patch sets automatically, with a brief maintenance window.

Dedicated Autonomous Database uses the four-layer provisioning hierarchy:

Autonomous Exadata Infrastructure (AEI): The physical Exadata rack. Configured with the number of database servers, storage servers, and the network configuration. The AEI is not shared with other tenants.

Autonomous VM Cluster (AVMC): The logical compute cluster on the AEI. Configured with the number of ECPUs allocated from the AEI and the memory allocation. Multiple AVMCs can share one AEI.

Autonomous Container Database (ACDB): The Oracle Database container on the AVMC. Each ACDB is an isolated multi-tenant Oracle Database. Container databases can be patched independently. Multiple ACDBs run on one AVMC.

Autonomous Database (ADB): The individual pluggable database within the ACDB. This is the unit that application schemas and users interact with. Multiple ADBs share one ACDB.

The exam tests this hierarchy in provisioning order and management scenarios – given a described management task, identify at which layer it is performed.

Domain 3: Performance Monitoring and Optimisation

Automatic Indexing: Autonomous Database continuously monitors query performance and automatically creates, rebuilds, or drops indexes based on workload analysis – without DBA intervention. The exam tests Automatic Indexing as the primary performance self-management capability that distinguishes ADB from manually-managed Oracle Database. It also tests what the DBA can do to influence Automatic Indexing – enabling or disabling it for the instance, accepting or rejecting individual auto-created indexes after reviewing their impact.

Performance Hub: Performance Hub is the OCI console performance monitoring dashboard for Autonomous Database – showing ASH (Active Session History) analytics, top SQL by elapsed time and resource consumption, wait event breakdowns, and real-time session activity. The exam tests Performance Hub as the primary tool for diagnosing performance problems in Autonomous Database, replacing the manual AWR and ASH report generation workflow used on-premises.

Service names and concurrency: Autonomous Database exposes multiple database services, each tuned for different workload characteristics – high (for short-running, high-concurrency OLTP transactions), medium (for moderate concurrency), low (for long-running queries, parallelism enabled), and tpurgent (for time-critical TP transactions). The exam tests service name selection as the DBA’s tool for controlling query concurrency and parallelism behaviour.

Domain 4: Security – Data Safe, TDE, and Customer-Managed Keys

Oracle Data Safe: Data Safe is Oracle’s integrated cloud database security service – available at no additional charge with Autonomous Database. Data Safe provides: Security Assessment (evaluating the database configuration against Oracle’s security best practices and CIS benchmarks), User Assessment (identifying over-privileged users, default passwords, and dormant accounts), Sensitive Data Discovery (scanning the database for columns containing PII, financial data, or other regulated data types), Data Masking (replacing sensitive data values with realistic but non-real substitutes for non-production environments), and Audit Reports. The exam tests Data Safe as the comprehensive security assessment and governance tool, and tests each capability’s specific purpose.

Transparent Data Encryption (TDE): Autonomous Database encrypts all data at rest using TDE by default – no configuration required. The exam tests TDE as a default-on feature, not something the DBA must explicitly enable.

Customer-managed encryption keys: By default, Oracle manages the TDE master encryption key for Autonomous Database. Customers with regulatory requirements to control their own encryption keys can configure Autonomous Database to use a customer-managed key stored in OCI Vault – a Bring Your Own Key (BYOK) configuration. The exam tests BYOK as the correct security architecture when the customer must maintain exclusive custody of encryption keys.

Oracle Data Safe Audit: Autonomous Database generates a unified audit trail – recording all user login attempts, DDL operations, DML operations by privileged users, and policy violations. Data Safe collects this audit trail and provides pre-built compliance reports aligned to GDPR, PCI DSS, and other regulatory frameworks.

Domain 5: Select AI – Natural Language to SQL

Select AI is Oracle Autonomous Database’s built-in natural language to SQL capability – allowing users to ask questions in plain English (or other supported languages) that the system translates to SQL, executes, and returns results.

The AI profile: Before Select AI can be used, an AI profile must be created – defining which AI provider (OCI Generative AI, OpenAI, or another supported LLM provider) handles the natural language processing, which credential provides authentication to that provider, and which database tables or views are in scope for natural language querying.

Select AI actions: The SELECT AI syntax accepts five actions that determine what the system does with the natural language input:

narrate – the system generates SQL, executes it, and returns a natural language summary of the results (answering the question in plain text rather than returning a table of data).

runsql – the system generates SQL and executes it, returning the query result set directly (the most common operational action).

showsql – the system generates SQL but does not execute it, returning the SQL text for the DBA to review before running.

explainsql – the system generates SQL and provides a natural language explanation of what the SQL does (useful for validating the generated query’s intent).

chat – the system engages in a conversational interaction about the database schema or the question, without generating executable SQL.

The exam tests these five actions as specific knowledge – given a described user requirement, identify which Select AI action produces the described result.

Domain 6: AI Vector Search

AI Vector Search allows Autonomous Database to store, index, and search vector embeddings alongside traditional relational data – enabling semantic similarity search without a separate vector database. Oracle’s implementation is part of the converged database architecture: vectors, relational data, JSON, spatial, and graph data all live in the same Autonomous Database.

Vector embeddings: A vector embedding is a numerical representation of unstructured data (text, image, audio) that captures its semantic meaning in a high-dimensional mathematical space. Items with similar meaning are represented by vectors that are close to each other in this space. Embedding models (such as those from Cohere, OCI Generative AI, or other providers) convert raw data to embeddings.

VECTOR data type: Autonomous Database stores embeddings in a VECTOR column – a new Oracle Database data type that stores the numerical vector array. The VECTOR_DISTANCE function measures the similarity between two vectors, using distance metrics including Cosine similarity (measures angular distance – preferred for text similarity), Euclidean distance (measures straight-line distance in vector space), and Dot Product (a signed similarity measure).

The exam-tested distinction: The exam specifically notes that AI Vector Search uses Oracle’s VECTOR_DISTANCE function, not the pgvector operators used in PostgreSQL-compatible vector databases. Candidates who have worked with PostgreSQL vector search must unlearn the pgvector syntax for this exam.

HNSW and IVF indexes: Oracle supports two vector index types for approximate nearest-neighbour search: HNSW (Hierarchical Navigable Small World – a graph-based index providing very fast search at the cost of memory) and IVF (Inverted File – a centroid-based index that partitions the vector space, providing fast search with lower memory usage). The exam tests vector index type selection as a design decision based on memory budget and query latency requirements.

Domain 7: MCP Server Integration and Converged Data

MCP (Model Context Protocol) server integration: Autonomous Database 2026 includes a built-in MCP server that allows AI agents to interact with the database using the standardised Model Context Protocol. An AI agent can discover the database schema, query data, and execute operations through the MCP interface – without requiring the agent developer to write custom database connectors. The exam tests MCP server integration as the mechanism enabling AI agent-to-database interaction.

JSON Relational Duality Views: JSON Relational Duality Views allow the same data to be accessed and modified as either relational tables or JSON documents – simultaneously and transactionally consistently. A duality view exposes relational join results as a JSON object; updates to the JSON object are automatically translated back to relational DML operations. The exam tests duality views as the Oracle 23ai architecture for applications that need both document API access and relational query capabilities on the same data.

Domain 8: Migration and Data Integration

Oracle Data Pump (expdp/impdp): The primary tool for migrating Oracle Database to Autonomous Database for non-time-sensitive migrations where downtime is acceptable. Data Pump exports the source schema to a dump file, the dump file is uploaded to OCI Object Storage, and impdp loads it into Autonomous Database. The exam tests Data Pump as the standard migration path for Oracle-to-Oracle migrations where a maintenance window is available.

Zero Downtime Migration (ZDM): ZDM automates online migration to Autonomous Database with near-zero downtime – using Data Guard or GoldenGate for continuous replication during migration, with a brief cutover window at the end. The exam tests ZDM as the correct tool when migration downtime must be minimised and the database must remain available throughout the migration process.

Oracle GoldenGate: GoldenGate provides continuous, real-time data replication from source databases to Autonomous Database – appropriate for ongoing replication (feeding analytics) or as the replication layer within a ZDM migration. The exam tests GoldenGate as the replication tool for both migration and ongoing data integration scenarios.

Pre-migration Advisor: Before migrating to Autonomous Database, the Pre-migration Advisor analyses the source database for compatibility – identifying features, object types, or configurations that are not supported or work differently in Autonomous Database. The exam tests the Pre-migration Advisor as the first step in any Autonomous Database migration project.

5 Study Tips for Oracle 1Z0-931-26

• Study the five Select AI actions (narrate, runsql, showsql, explainsql, chat) as specific named operations – know exactly what each action does with the natural language input and which action produces which type of output.
• Study the Dedicated ADB four-layer hierarchy (AEI → AVMC → ACDB → ADB) as a provisioning dependency chain – know what is configured at each layer and in what order the layers must be created.
• Study AI Vector Search with Oracle’s VECTOR_DISTANCE function – not pgvector operators – and know the two index types (HNSW and IVF) and when each is the right choice.
• Study Oracle Data Safe capabilities by specific function name: Security Assessment, User Assessment, Sensitive Data Discovery, Data Masking, Audit Reports – know what each does and do not confuse them with each other.
• Practice with Cert Empire’s 1Z0-931-26 exam questions covering all eight domain areas at Select AI action syntax, Dedicated ADB provisioning hierarchy, AI Vector Search index design, and migration tool selection depth.

Best Study Resources

• Cert Empire 1Z0-931-26 exam questions PDF and practice simulator (October 2026 edition).
• Oracle official 1Z0-931-26 exam page (education.oracle.com – search “1Z0-931-26”).
• Oracle Autonomous Database documentation (docs.oracle.com/en/cloud/paas/autonomous-database/).
• Oracle Select AI documentation (docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/sql-generation-ai-autonomous.html).
• Oracle AI Vector Search documentation (docs.oracle.com/en/database/oracle/oracle-database/23/vecse/).

Why Candidates Choose Cert Empire for Oracle 1Z0-931-26 Preparation

Free Practice Tests

Sample our 1Z0-931-26 questions before purchasing. Test your Select AI action knowledge, Dedicated ADB hierarchy understanding, and AI Vector Search index type reasoning before committing.

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Every purchase includes our free 1Z0-931-26 exam guide covering all eight domain areas, the Select AI action reference table, the Dedicated ADB hierarchy diagram, and a structured study plan.

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Available at any hour for questions about your materials, your order, or specific Autonomous Database concepts – including Select AI profile configuration, AI Vector Search VECTOR_DISTANCE syntax, and Zero Downtime Migration workflow.

3 Months of Unlimited Access

Three full months of unlimited access to the 1Z0-931-26 question bank and simulator. Revisit Automatic Indexing scenarios, Data Safe capability questions, and migration tool selection topics as many times as needed.

Official Vendor Resources – Learn from the Source

Every solution in our 1Z0-931-26 question bank links directly to the relevant official Oracle documentation – the Autonomous Database developer guide, Oracle Data Safe documentation, or the specific Oracle 23ai feature reference behind the answer.

Deep Knowledge – Every Answer, Every Option, Fully Explained

Every 1Z0-931-26 question includes a complete explanation covering not just why the correct Select AI action, vector index type, or migration tool is right, but precisely why each incorrect option fails – whether it uses explainsql where runsql is needed for a result set, selects HNSW where IVF is the memory-constrained choice, or applies Data Pump where ZDM is required for a zero-downtime migration. Each explanation references the specific Oracle Autonomous Database documentation behind the answer.

Interactive Practice Simulator – Think Like the Exam

Our simulator presents Autonomous Database implementation scenarios exactly as the real 1Z0-931-26 exam does – describing an AI feature requirement, a performance tuning problem, or a migration constraint and testing which Select AI action, Data Safe capability, database service, or migration tool correctly addresses it. Per-question feedback explains the full Autonomous Database reasoning behind every correct and incorrect option.

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FAQ’s

What is the Oracle 1Z0-931-26 certification?

1Z0-931-26 is the Oracle Autonomous AI Database Professional exam. It validates expertise in provisioning, managing, securing, optimising, and migrating Oracle Autonomous Database – with specific 2026 emphasis on AI capabilities including Select AI natural language to SQL, AI Vector Search, and MCP server integration for AI agents.

What are the five Select AI actions?

The five Select AI actions are: narrate (execute the generated SQL and return a natural language summary of the results), runsql (execute the generated SQL and return the result set), showsql (return the generated SQL without executing it), explainsql (return the generated SQL with a natural language explanation of what it does), and chat (conversational interaction about the schema or question without SQL generation).

What is the Dedicated Autonomous Database provisioning hierarchy?

The four-layer hierarchy is: Autonomous Exadata Infrastructure (the physical rack) → Autonomous VM Cluster (the logical compute cluster on the rack) → Autonomous Container Database (the Oracle Database container providing isolation) → Autonomous Database (the individual database instance). Each layer must be provisioned before the layer above it can be created.

What is AI Vector Search in Autonomous Database?

AI Vector Search allows Autonomous Database to store vector embeddings in a VECTOR column and search for semantically similar items using the VECTOR_DISTANCE function. It supports Cosine similarity, Euclidean distance, and Dot Product distance metrics. Vector indexes (HNSW or IVF) enable approximate nearest-neighbour search for performance. Oracle’s implementation uses VECTOR_DISTANCE, not pgvector operators.

What is the difference between HNSW and IVF vector indexes?

HNSW (Hierarchical Navigable Small World) is a graph-based vector index providing very fast approximate nearest-neighbour search at the cost of higher memory usage. IVF (Inverted File) is a centroid-based index that partitions the vector space, providing fast search with lower memory requirements. HNSW is preferred when memory is abundant and maximum query speed is the priority; IVF is preferred when memory is constrained.

What is Oracle Data Safe?

Oracle Data Safe is an integrated cloud database security service included with Autonomous Database. Its capabilities are Security Assessment (configuration review), User Assessment (privilege and account review), Sensitive Data Discovery (PII and regulated data scanning), Data Masking (replacing sensitive values with masked substitutes), and Audit Reports (compliance-aligned audit trail reporting).

How many questions and what is the passing score for 1Z0-931-26?

The exam has 50 questions, the passing score is 68%, and the duration is 90 minutes. It is delivered through Pearson VUE either online-proctored or at a test centre. The cost is USD $245.

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