Browse Exams — Mock Exams & Practice Tests

1Z0-931-25 Syllabus — Learning Objectives by Topic

Learning objectives for Oracle AI Autonomous Database 2025 Professional (1Z0-931-25), organized by topic with quick links to targeted practice.

Use this syllabus as your checklist for 1Z0‑931‑25.

What’s covered

Topic 1: Autonomous Database Architecture and Provisioning

Practice this topic →

1.1 Service options and workload fit

  • Differentiate Autonomous Transaction Processing vs Autonomous Data Warehouse at a conceptual level.
  • Explain serverless vs dedicated deployment models and when each is appropriate.
  • Given a scenario, choose an Autonomous Database option based on workload patterns and governance needs.
  • Identify shared responsibility boundaries for Autonomous Database vs customer-managed databases.
  • Recognize common non-functional requirements that drive service selection (HA, compliance, connectivity).

1.2 Networking and connectivity fundamentals

  • Explain public vs private access patterns and how they affect exposure and operational complexity.
  • Given a scenario, choose a network design that meets security and connectivity requirements.
  • Recognize common connectivity components: VCNs, subnets, gateways, and allowlists.
  • Identify why DNS, routing, and security lists/NSGs can cause connection failures.

1.3 Scaling, resource management, and workload isolation

  • Explain how compute scaling and storage scaling impact performance and cost.
  • Given a scenario, choose scaling approaches that preserve SLAs under peak load.
  • Recognize why concurrency and resource limits are required to protect multi-tenant workloads.
  • Identify capacity planning considerations for batch loads, reporting peaks, and mixed workloads.

1.4 Cloning, environments, and lifecycle basics

  • Explain why cloning supports dev/test/prod parity and safe change management.
  • Given a scenario, choose full clone vs metadata-only approaches where applicable.
  • Recognize when to refresh lower environments from production for realistic performance testing.
  • Identify lifecycle activities: provisioning, scaling, patching, backups, and decommissioning.

Topic 2: Data Loading and Integration

Practice this topic →

2.1 Bulk loading from files and object storage

  • Explain common loading patterns: staged files, external tables, and direct loads.
  • Given a scenario, choose a load method based on file size, frequency, and validation needs.
  • Recognize the role of credentials/secrets when accessing object storage or external sources.
  • Identify validation steps after load: row counts, checksums, and constraint checks.
  • Explain why file formats and partitioning choices can affect load speed and query performance.

2.2 Database connectivity and migration approaches

  • Differentiate logical vs physical migration methods at a conceptual level.
  • Given a scenario, choose a migration approach based on downtime tolerance and data volume.
  • Recognize why network throughput and latency are critical to migration timelines.
  • Identify common migration risks: incompatible features, missing privileges, and data type mismatches.

2.3 Replication and near-real-time integration patterns

  • Explain why change data capture is used for low-latency replication and analytics feeds.
  • Given a scenario, choose replication vs batch loads to meet freshness requirements.
  • Recognize common replication considerations: ordering, conflict handling, and schema changes.
  • Identify operational signals: replication lag, error queues, and reconciliation metrics.

2.4 Schema management, constraints, and data quality controls

  • Explain why constraints (PK/FK/UNIQUE/CHECK) improve data quality and query optimization.
  • Given a scenario, choose when to enforce constraints during load vs after staging.
  • Recognize schema drift and how it can break ingestion pipelines and downstream reports.
  • Identify strategies for handling bad records (quarantine tables, error logs, retry workflows).

Topic 3: Performance, Optimization, and Workload Management

Practice this topic →

3.1 Observability and performance troubleshooting

  • Identify the key performance signals: CPU, I/O, wait events, concurrency, and SQL hotspots.
  • Given a scenario, choose the first diagnostic step (slow SQL analysis vs resource saturation check).
  • Explain why baselines are required to detect regressions after changes.
  • Recognize how workload mix (OLTP vs analytics) changes the tuning approach.

3.2 Indexing, partitioning, and data layout

  • Explain why indexing choices impact both read performance and write cost.
  • Given a scenario, choose partitioning strategies that support pruning and manage large tables.
  • Recognize common index anti-patterns: over-indexing and indexing low-cardinality columns.
  • Identify when to redesign data layout vs tune individual SQL statements.

3.3 SQL tuning and optimizer fundamentals

  • Explain how statistics and cardinality estimates influence execution plans.
  • Given a scenario, identify why a query regressed (data skew, stale stats, plan change).
  • Recognize common plan operations: joins, sorts, hash aggregation, and full scans.
  • Identify tuning levers: query rewrite, indexing, partitioning, and predicate simplification.
  • Explain why testing with representative data volumes matters for optimizer behavior.

3.4 Resource controls and workload isolation

  • Explain the need for workload management to protect critical workloads from noisy neighbors.
  • Given a scenario, choose concurrency limits or scheduling strategies for heavy batch jobs.
  • Recognize how long-running queries can impact overall system throughput.
  • Identify when to scale resources vs optimize queries based on evidence.

Topic 4: Security, Compliance, and Data Protection

Practice this topic →

4.1 Identity, access, and privilege management

  • Apply least privilege to database users/roles and administrative operations.
  • Given a scenario, choose authentication and authorization approaches that meet enterprise requirements.
  • Recognize why separation of duties reduces risk for production databases.
  • Identify common privilege pitfalls: excessive grants, shared accounts, and unmanaged credentials.

4.2 Network security and exposure control

  • Explain why private access reduces exposure for sensitive database workloads.
  • Given a scenario, choose network controls that restrict who can connect and from where.
  • Recognize common connectivity misconfigurations that create either outages or unintended exposure.
  • Identify when to use allowlists, private endpoints, and tiered network segmentation.

4.3 Encryption, key management, and secrets

  • Explain encryption at rest and in transit and where each is enforced in database architectures.
  • Given a scenario, choose customer-managed keys vs provider-managed keys based on compliance requirements.
  • Recognize how key rotation and secret rotation reduce long-term compromise risk.
  • Identify why connection strings, wallets, and API keys must be protected and audited.

4.4 Auditing, masking, and security operations

  • Explain why auditing is required for investigations and regulatory compliance.
  • Given a scenario, choose what to audit (privileged actions, schema changes, sensitive table access).
  • Recognize when to use masking/redaction to reduce exposure of sensitive fields.
  • Identify how security tooling and alerts support ongoing posture management.
  • Explain why logs must be retained and protected from tampering.

Topic 5: Availability, Backup, and Disaster Recovery

Practice this topic →

5.1 Backup strategy and point-in-time recovery

  • Explain RPO/RTO and how they map to backup and recovery design.
  • Given a scenario, choose restore approaches that meet outage vs corruption recovery needs.
  • Recognize why regular restore testing is necessary to validate recovery readiness.
  • Identify the operational steps required after restore (validation, reconnect apps, reconcile data).

5.2 High availability and cross-region DR concepts

  • Differentiate high availability within a region from disaster recovery across regions.
  • Given a scenario, choose failover strategies that meet RTO and operational complexity constraints.
  • Recognize the trade-offs between synchronous and asynchronous protection (concept-level).
  • Identify common DR risks: DNS cutover, application dependency mapping, and stale runbooks.
  • Explain why DR drills should include both failover and failback planning.

5.3 Maintenance, patching, and change management

  • Explain why patching and maintenance must be planned to minimize risk and downtime.
  • Given a scenario, choose maintenance windows and notification practices appropriate for business impact.
  • Recognize rollback considerations for database changes (schema migrations, parameter changes).
  • Identify why change approvals and documentation reduce incidents.

5.4 DR validation and incident response

  • Design DR tests that validate both data correctness and application functionality.
  • Given a scenario, identify containment actions during major incidents (freeze changes, isolate access).
  • Explain post-incident steps: root cause analysis, runbook updates, and control improvements.
  • Recognize when to prioritize recovery speed vs data integrity and how to communicate trade-offs.

Topic 6: AI/ML and Advanced Data Capabilities

Practice this topic →

6.1 In-database ML concepts and workflows

  • Explain when in-database ML is preferred (data gravity, governance, operational simplicity).
  • Given a scenario, choose a workflow for training, evaluating, and registering an ML model.
  • Recognize common model lifecycle steps: feature prep, training, validation, deployment, monitoring.
  • Identify risks: leakage, biased training data, and missing evaluation guardrails.
  • Explain why model versioning and approval gates reduce production risk.

6.2 Analytical SQL and data transformation inside the database

  • Use analytical reasoning to choose window functions vs GROUP BY for common reporting needs.
  • Given a scenario, choose database-native transforms to reduce data movement.
  • Recognize performance implications of heavy transformations (sorts, joins, large aggregations).
  • Identify when to materialize intermediate results vs compute on demand.

6.3 Modern data types and AI-enabling features

  • Explain why vector representations enable similarity search and retrieval workflows (concept-level).
  • Given a scenario, choose between relational joins and similarity search for different tasks.
  • Recognize how JSON/semi-structured data impacts indexing and query patterns.
  • Identify governance needs for AI-enabling features (access control, auditing, data retention).
  • Explain why evaluation and monitoring remain necessary even when AI runs inside the database.

6.4 Responsible AI and enterprise governance considerations

  • Identify responsible AI practices relevant to data platforms: traceability, review, and access boundaries.
  • Given a scenario, choose controls for sensitive training data (masking, minimization, segmentation).
  • Explain why reproducibility matters for auditability (dataset versions, feature logic, model versions).
  • Recognize when to require human approval for high-impact model outputs.

Topic 7: Operations, Automation, and Cost

Practice this topic →

7.1 Monitoring, alerting, and service health

  • Define operational metrics: availability, performance, error rates, backup status, and replication lag.
  • Given a scenario, choose alert thresholds that detect incidents without noisy false positives.
  • Recognize why log redaction and access controls are required for sensitive database telemetry.
  • Identify leading indicators of issues: rising waits, storage pressure, and failing background tasks.

7.2 Automation with APIs, CLI, and infrastructure-as-code

  • Explain why automation reduces configuration drift and speeds recovery.
  • Given a scenario, design an automated workflow for provisioning, scaling, and policy enforcement.
  • Recognize the need for idempotent automation and safe retries.
  • Identify secrets management practices for automation credentials and service principals.
  • Explain how tagging and compartment standards support automated governance.

7.3 Cost management and capacity planning

  • Identify cost drivers: compute usage, storage, data egress, and long-running workloads.
  • Given a scenario, choose scaling and scheduling tactics that reduce spend without breaking SLAs.
  • Explain why workload consolidation can be risky without resource controls and monitoring.
  • Recognize when to optimize queries vs scale resources based on evidence.

7.4 Troubleshooting common issues

  • Diagnose authentication/authorization failures by checking identities, policies, and credentials.
  • Given a scenario, troubleshoot connectivity issues (routing, security rules, DNS, allowlists).
  • Identify common ingestion failures: format errors, permission problems, and schema drift.
  • Recognize when performance issues require query tuning vs scaling vs workload isolation.
Study Plan
Cheat Sheet