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1Z0-1093-25 Syllabus — Learning Objectives by Topic

Learning objectives for Oracle AI Cloud Database Services 2025 Professional (1Z0-1093-25), organized by topic with quick links to targeted practice.

Use this syllabus as your checklist for 1Z0‑1093‑25.

What’s covered

Topic 1: Oracle Cloud Database Services Portfolio and Architecture

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1.1 Cloud database service options and workload fit

  • Differentiate common Oracle cloud database service options at a high level (managed DB systems, Exadata-based services, autonomous offerings).
  • Given a scenario, choose an appropriate service based on performance requirements and operational responsibility.
  • Explain shared responsibility boundaries for managed services vs customer-managed configuration choices.
  • Recognize typical enterprise requirements that drive architecture (latency, HA/DR, compliance, connectivity).

1.2 Compute, storage, and scaling concepts

  • Explain how CPU, memory, and I/O constraints shape database performance profiles.
  • Given a scenario, choose scaling approaches (scale up vs scale out) consistent with service capabilities.
  • Recognize the cost/performance trade-off of overprovisioning vs right-sizing with monitoring.
  • Identify why storage layout, throughput, and latency matter for batch loads and reporting peaks.

1.3 Networking and connectivity patterns for databases

  • Differentiate public vs private access patterns and their security implications.
  • Given a scenario, choose network designs that support on-prem connectivity and segmentation.
  • Recognize common connectivity components: VCNs, subnets, routing, gateways, and allowlists.
  • Identify common causes of database connectivity failures (DNS, routing, security rules, ports).

1.4 Architecture trade-offs (performance, resiliency, compliance)

  • Given a scenario, choose between single-instance, clustered, or protected-by-standby designs based on availability goals.
  • Explain why RPO/RTO should drive DR architecture rather than “best effort” replication.
  • Recognize when strict compliance requirements constrain network exposure, key management, and logging.
  • Identify how data gravity affects architecture (keep compute close to data; minimize movement).
  • Explain why standardization (naming, tagging, compartments) improves governance at scale.

Topic 2: Provisioning and Configuration

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2.1 Provision database systems (shapes, storage, images)

  • Identify required inputs for provisioning: network placement, compute shape, storage sizing, and access controls.
  • Given a scenario, choose a shape and storage configuration that meets throughput and capacity needs.
  • Recognize the impact of provisioning choices on maintainability (patching model, backups, scaling).
  • Explain why consistent configuration standards reduce incident risk across environments.

2.2 Database configuration fundamentals

  • Explain core database configuration areas: memory/CPU usage, redo/undo behavior, and connection management (concept-level).
  • Given a scenario, choose configuration choices that support workload patterns (many small transactions vs large analytics).
  • Recognize why multitenant concepts (containers and pluggable databases) matter for consolidation (concept-level).
  • Identify configuration drift risks and how to detect them with automation and baselines.

2.3 Patching, maintenance windows, and upgrade strategy

  • Explain why patching strategy must balance security, stability, and downtime constraints.
  • Given a scenario, choose maintenance windows and notification practices appropriate for business impact.
  • Recognize upgrade risks: incompatible features, application dependencies, and insufficient testing.
  • Identify rollback planning needs for database upgrades and major configuration changes.

2.4 Environment management (dev/test/prod) and cloning

  • Explain why dev/test/prod separation is required for risk management and compliance.
  • Given a scenario, choose cloning/refresh approaches that preserve realistic performance testing.
  • Recognize the importance of masking/sanitization when using production data in lower environments.
  • Identify lifecycle practices: create, scale, patch, backup, rotate secrets, and decommission.
  • Explain why standard runbooks and checklists reduce errors during changes.

Topic 3: High Availability and Disaster Recovery

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3.1 HA concepts and clustered designs

  • Differentiate availability within a region from resilience across regions.
  • Given a scenario, choose architectures that reduce single points of failure (redundant tiers, fault domain placement).
  • Recognize when clustering or multi-node designs are appropriate for uptime and scalability goals (concept-level).
  • Identify operational trade-offs: complexity, patching coordination, and failure modes.

3.2 Standby and replication approaches (Data Guard concepts)

  • Explain the purpose of standby databases and how they support DR and read scaling (concept-level).
  • Given a scenario, choose synchronous vs asynchronous protection based on RPO/RTO needs (concept-level).
  • Recognize common replication risks: lag, diverging configurations, and schema changes.
  • Identify monitoring requirements: apply lag, transport errors, and role transitions.

3.3 Cross-region DR planning and dependency mapping

  • Create a DR plan that includes application dependencies (DNS, identity, network, secrets, and monitoring).
  • Given a scenario, choose DR topologies that meet business continuity requirements.
  • Recognize why DR tests must validate application functionality, not just database role changes.
  • Identify failback planning needs and risks (data loss windows, configuration drift).

3.4 DR execution, testing, and runbooks

  • Explain why DR runbooks must be deterministic and regularly updated after changes.
  • Given a scenario, choose DR drill frequency and scope based on risk and regulatory requirements.
  • Recognize when to prioritize recovery speed vs data integrity and how to communicate trade-offs.
  • Identify common DR failure points: DNS cutover, stale credentials, and missing network routes.
  • Explain why post-DR drill reviews should feed back into automation and monitoring improvements.

Topic 4: Backup, Restore, and Migration

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4.1 Backup strategy design

  • Define RPO/RTO and map them to backup frequency, retention, and storage choices.
  • Given a scenario, choose backup approaches for small vs large databases (concept-level).
  • Recognize why backup encryption, access controls, and immutability reduce ransomware risk.
  • Identify why restore testing is required to validate backup integrity.

4.2 Restore scenarios and data recovery

  • Differentiate recovery from accidental deletion, logical corruption, and infrastructure failure.
  • Given a scenario, choose point-in-time recovery vs full restore based on data loss tolerance.
  • Recognize operational steps after restore: validation, reconnect applications, reconcile downstream systems.
  • Identify common restore risks: missing logs, incorrect credentials, and untested runbooks.

4.3 Migration methods and cutover planning

  • Differentiate logical migration vs physical migration and when each is appropriate (concept-level).
  • Given a scenario, choose online vs offline migration based on downtime and data volume constraints.
  • Recognize common migration issues: data type differences, feature incompatibilities, and performance regressions.
  • Identify pre-cutover checks: network readiness, user/privilege mapping, and application compatibility.

4.4 Validation and rollback for migrations

  • Design validation checks for migrations: row counts, checksums, and application-level smoke tests.
  • Given a scenario, choose rollback strategies that minimize downtime and prevent split-brain writes.
  • Recognize why dual-write or replication cutovers require careful conflict handling.
  • Identify post-cutover monitoring signals that detect hidden issues (latency spikes, error rates).
  • Explain why migration runbooks must include decision points and clear success criteria.

Topic 5: Security, Compliance, and Data Protection

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5.1 Identity and access controls

  • Apply least privilege to administrative access and application database users.
  • Given a scenario, choose authentication and authorization approaches that meet enterprise policy requirements.
  • Recognize separation of duties needs between database administrators, security teams, and application owners.
  • Identify common access risks: shared accounts, unmanaged secrets, and overbroad grants.

5.2 Network security and segmentation

  • Explain why private connectivity and segmentation reduce attack surface for databases.
  • Given a scenario, choose network controls: allowlists, private subnets, and tiered boundaries.
  • Recognize common misconfigurations that cause exposure or outages (routes, security lists, ports).
  • Identify when to restrict administrative access paths separately from application access.

5.3 Encryption, keys, and secrets management

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

5.4 Auditing, masking, and security operations

  • Design auditing for privileged actions, schema changes, and sensitive table access.
  • Given a scenario, choose what to log and retain to support investigations and compliance.
  • Recognize when to use masking/redaction to reduce exposure of sensitive data in lower environments.
  • Identify how to integrate security alerts with incident response workflows.
  • Explain why audit logs must be protected from tampering and unauthorized deletion.

Topic 6: Performance, Scaling, and Operations

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6.1 Monitoring and performance diagnosis

  • Identify key performance signals: CPU, I/O, waits, concurrency, and SQL hotspots.
  • Given a scenario, choose the correct first diagnostic step (top SQL vs resource saturation vs locking).
  • Explain why baselines and trend monitoring are required to detect regressions.
  • Recognize how workload mix changes tuning priorities (OLTP vs analytics vs mixed).

6.2 SQL tuning and data layout fundamentals

  • Explain how statistics and cardinality estimates influence execution plans (concept-level).
  • Given a scenario, choose tuning levers: query rewrite, indexing, partitioning, or resource scaling.
  • Recognize common anti-patterns: missing predicates, over-indexing, and unbounded joins.
  • Identify when to redesign schema or data layout rather than tuning individual statements.

6.3 Capacity planning and scaling strategies

  • Identify capacity drivers: peak concurrency, batch windows, and reporting scan volume.
  • Given a scenario, choose scaling tactics that meet SLAs while controlling cost.
  • Explain why headroom is required for failures, backfills, and maintenance events.
  • Recognize when to separate workloads across systems to prevent interference.

6.4 Reliability practices and maintenance operations

  • Explain why maintenance windows must align with business impact and operational readiness.
  • Given a scenario, choose patching and change strategies that reduce risk (staged rollout, rollback plan).
  • Recognize the importance of runbooks and automation for repeatable operations.
  • Identify health checks and guardrails that should block risky changes.
  • Explain why post-change monitoring should be heightened to catch regressions early.

Topic 7: Automation and Troubleshooting

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7.1 Infrastructure-as-code and automation design

  • Explain why infrastructure-as-code reduces drift and improves repeatability.
  • Given a scenario, design automation that is idempotent and safe to retry.
  • Recognize secret handling requirements for automation (no hard-coded credentials; rotation).
  • Identify governance benefits of automation: consistent tagging, compartments, and policy enforcement.

7.2 Operational monitoring and alerting

  • Design alerting for availability, backups, replication lag, storage pressure, and security anomalies.
  • Given a scenario, tune alerts to reduce noise while preserving fast detection.
  • Recognize why logs must be redacted and access-controlled to avoid data leaks.
  • Identify common dashboards for DB ops: top SQL, wait events, throughput, and error rates.
  • Explain why SLOs and runbooks should be tied to alerts.

7.3 Incident response and postmortems

  • Given a scenario, choose containment actions for incidents (freeze changes, isolate access, fail over).
  • Explain why communication and timeline logging are critical during major incidents.
  • Recognize common incident categories: performance collapse, data corruption, and credential compromise.
  • Identify postmortem outputs: root cause, contributing factors, and preventive controls.

7.4 Troubleshooting common failures

  • Troubleshoot authentication/authorization failures by validating identities, privileges, and secrets.
  • Given a scenario, diagnose connectivity failures using routing, security rules, DNS, and allowlists.
  • Identify backup/restore failures caused by missing logs, permissions, or incorrect recovery steps.
  • Recognize when performance issues require query tuning vs scaling vs workload isolation.
  • Explain why changes should be rolled back quickly when guardrail metrics regress.
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