Pass the Oracle Cloud Infrastructure 1z0-1147 Questions and answers with ExamsMirror

When Oracle Autonomous AI Database Serverless is provisioned through Oracle AI Database@Azure, the administrative database account follows the standard Autonomous Database account model. The Azure creation workflow exposes the Username field as read-only and set to ADMIN .

The administrator therefore supplies the password for the predefined ADMIN account but does not choose or rename the account during database creation. Oracle currently specifies password requirements separately, including minimum and maximum length and character-composition requirements.

The account is not derived from the Microsoft Entra ID identity that deploys the Azure resource. Azure RBAC and Microsoft Entra authentication govern access to the Azure resource-management plane, whereas the ADMIN credential is a database-level account. Similarly, SYS is not presented as the configurable provisioning username for Autonomous AI Database Serverless, and the username is not generated from subscription metadata.

This separation between Azure control-plane identity and Oracle database credentials is architecturally important when designing authentication and administrative access.

Therefore, the correct answer is A .

Study Guide reference: Provision and operate Oracle AI Database@Azure Resources — Autonomous AI Database Serverless creation, configuration and administrator credentials.

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This configuration is explicitly identified as unsupported in Microsoft's Oracle AI Database@Azure network-planning guidance. If two Oracle AI Database@Azure VNets are deployed in the same Oracle Availability Zone and one uses Advanced Networking while the other uses the default networking implementation, VNet peering between them can introduce asymmetric routing .

Asymmetric routing occurs when outbound and return traffic traverse different network paths. This is particularly problematic with stateful network elements and the differing routing models associated with the two Oracle AI Database@Azure network implementations. The result can be data-plane traffic loss toward the VNet using Advanced Networking.

VNet peering itself does not upgrade a previously provisioned VNet to Advanced Networking, nor does it downgrade the Advanced Networking environment. Advanced Networking capability is determined by the feature configuration and the state of the delegated subnet before Oracle resources/VNICs are provisioned.

Therefore, the two environments should not be directly peered in this mismatched state. The correct answer is A .

Study Guide reference: Configure Oracle AI Database@Azure Networking — Advanced Networking compatibility and VNet peering.

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Oracle Exadata Database Service on Exascale Infrastructure uses the Elastic Compute Processing Unit (ECPU) model for allocating database-server processing capacity. Oracle's Exascale technical architecture specifies that a VM requires at least eight enabled ECPUs for initial VM Cluster provisioning and that vertical compute scaling is performed in increments of four ECPUs .

This model provides substantially finer-grained scaling than traditional fixed Exadata infrastructure shapes while maintaining predictable resource allocation across the underlying Exascale compute pool. Administrators can increase enabled ECPUs as workload demand grows, and Exascale also supports reserved ECPU concepts that allow capacity to be physically reserved for future growth. The distinction between enabled and reserved capacity is operationally important: enabled ECPUs are active compute resources used by the VM, whereas reserved capacity can facilitate future scale-up.

An increment of eight would unnecessarily double the required scaling step; one- or two-ECPU increments are not the documented unit for Exadata VM scaling on Exascale Infrastructure. The correct granularity is therefore four ECPUs .

Study Guide reference: Provision and operate Oracle AI Database@Azure Resources — Exadata Database Service on Exascale Infrastructure, ECPU compute model, VM Cluster provisioning and scaling.

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For an Autonomous AI Database Serverless target, Oracle Zero Downtime Migration supports Logical Online and Logical Offline migration workflows.

Logical migration is necessary because Autonomous AI Database Serverless abstracts the underlying physical database infrastructure and does not expose the file-system and database-host control required for a conventional physical RMAN/Data Guard migration. ZDM therefore operates through logical database migration technologies.

Logical Offline uses Oracle Data Pump export and import to instantiate the target. Logical Online adds Oracle GoldenGate to the workflow so changes occurring on the source after the initial data load are captured and applied to the target until final cutover, greatly reducing application downtime.

Oracle's Oracle Database@Azure migration documentation explicitly lists these two workflows for migration to Autonomous Database Serverless. Physical Online and Physical Offline workflows apply to supported customer-managed Oracle database targets, such as Exadata Database Service, rather than Autonomous Serverless.

Therefore, A is correct.

Study Guide reference: Migrate Oracle Databases to Oracle AI Database@Azure — Autonomous AI Database Serverless migration workflow support.

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Oracle AI Database@Azure applies stricter hostname-prefix validation than the generic 63-character DNS-label maximum. For an Exadata VM Cluster, Oracle's DNS and network-design documentation specifies a maximum hostname-prefix length of 23 characters .

The value also has syntactic constraints. It must begin with a letter, end with an alphanumeric character, and can contain hyphens. Certain reserved values are prohibited. These restrictions exist because the supplied prefix becomes part of system-generated host and database-network names; Oracle must reserve additional character capacity for generated suffixes and service-specific naming constructs.

Therefore, 63 characters represents a general DNS-label ceiling but is not valid for this Exadata field. Thirty characters likewise exceeds the Oracle-specific limit, while 15 characters is unnecessarily restrictive.

The correct maximum is consequently 23 characters .

This is a configuration detail worth memorizing because hostname validation occurs during VM Cluster provisioning and an invalid prefix can block resource creation before the underlying Exadata compute nodes are deployed.

Study Guide reference: Configure Oracle AI Database@Azure Networking — Exadata VM Cluster DNS, hostname prefix rules and naming constraints.

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Oracle AI Database@Azure has specific client-subnet IP consumption requirements that must be considered during capacity planning. For the delegated client subnet, 13 IP addresses are reserved for networking services regardless of the number of VM Clusters deployed in that subnet .

Microsoft's Oracle AI Database@Azure networking documentation defines these 13 addresses as the first four addresses, the ninth through sixteenth addresses, and the final address of the subnet. Applied to 10.0.0.0/24, the reserved ranges are therefore 10.0.0.0–10.0.0.3, 10.0.0.8–10.0.0.15, and 10.0.0.255.

These platform reservations must be counted in addition to addresses needed for VM nodes and SCAN addresses. For example, every VM consumes multiple client-network addresses, and each VM Cluster requires SCAN addresses. Failing to account for the reserved block can therefore result in insufficient address capacity even when a subnet initially appears large enough.

Only option B precisely represents the documented reserved address positions.

Study Guide reference: Configure Oracle AI Database@Azure Networking — delegated subnet planning, client subnet sizing, reserved networking-service IP addresses and Exadata VM Cluster capacity.

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The correct solution combines Oracle Database Autonomous Recovery Service Long-Term Retention (LTR) with Retention Lock .

Recovery Service permits an LTR backup to be retained for up to 3,650 days—10 years . LTR backups are independent of the normal operational backup window and are designed specifically for compliance, regulatory, legal, and archival requirements.

Retention Lock adds the required immutability control. Oracle describes Retention Lock as an enforcement mechanism that protects retained backups against inadvertent changes and malicious actions such as ransomware. Once governed by the locked protection policy, backup retention cannot simply be shortened or deleted through normal backup-administration privileges before the protected period expires.

A 60-day Object Storage policy fails the ten-year requirement. Manual NFS backups lack the managed LTR and retention-lock framework, while Data Guard standby databases solve availability and disaster-recovery problems rather than replacing governed backup retention.

Therefore, C uniquely satisfies both long-term retention and immutability.

Study Guide reference: Secure Oracle AI Database@Azure — Autonomous Recovery Service, LTR and Retention Lock.

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Oracle Autonomous Container Database backup configuration includes an explicit backup-retention policy on termination . Administrators can select one of two supported behaviors: retain backups according to the normal configured backup-retention period, or retain them for 72 hours after termination and then delete them .

Oracle documentation identifies these options as RETAIN_PER_RETENTION_WINDOW and RETAIN_FOR_72_HOURS. The latter is the default in relevant API/Terraform configuration. Therefore, terminating an Autonomous Container Database does not inherently result in the immediate deletion of all automatic backups. Their post-termination lifetime is governed by the configured termination-retention policy.

Standard automatic backups are also not automatically converted into Long-Term Retention backups when the database is terminated. LTR backups have their own lifecycle and are explicitly excluded from the ordinary ACD termination-retention setting. No automatic export to Azure Blob Storage occurs as part of ACD termination.

Thus, B correctly reflects Oracle's documented post-termination backup behavior.

Study Guide reference: Secure Oracle AI Database@Azure — Autonomous Container Database automatic backups, termination and retention policy.

Oracle Autonomous AI Database is specifically designed for workloads where elasticity and minimal administrative overhead are primary requirements. Oracle describes it as a fully autonomous, self-governing database service that handles provisioning, backups, patching, upgrading, performance management, and resource scaling without traditional database-administration effort.

For unpredictable workloads, compute auto scaling is particularly significant. When enabled, Autonomous AI Database can automatically consume additional CPU and I/O resources as workload demand rises without requiring an administrator to manually resize the database. Storage can likewise be scaled and can use separate auto-scaling functionality.

Exadata Database Service on Dedicated Infrastructure provides extremely high performance and consolidation capabilities but retains customer database-management responsibilities. Exascale Infrastructure improves Exadata's elastic infrastructure model but is still not equivalent to Autonomous Database's fully managed, self-governing operational model. Base Database Service similarly requires substantially more customer administration.

Therefore, a cloud-native application with volatile usage and a requirement for essentially zero database administration maps directly to Oracle Autonomous AI Database .

Study Guide reference: Provision and operate Oracle AI Database@Azure Resources — Autonomous AI Database, elastic scaling, automated management and serverless operations.

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For a cross-region Autonomous Data Guard standby , Oracle specifies a maximum possible data-loss objective of approximately 1 minute . Accordingly, the intended Recovery Point Objective is one minute.

Cross-region protection differs from a local Autonomous Data Guard standby because geographical distance makes fully synchronous zero-data-loss transport impractical for normal cross-region operation. Oracle therefore uses an asynchronous protection model that provides a bounded RPO while maintaining acceptable database performance.

A useful distinction is between RPO and RTO. RPO measures potential data loss, whereas RTO measures how long restoration of service is targeted to take. Oracle's current Autonomous AI Database Serverless SLO documentation continues to specify a 1-minute cross-region RPO . Current documentation has improved the cross-region RTO target to less than 10 minutes ; older material cited approximately 15 minutes. That RTO evolution does not affect the answer to this question because only RPO is being tested.

Zero data loss therefore applies to different protection scenarios, while five and fifteen minutes do not represent the documented cross-region RPO.

Study Guide reference: HA and DR — Autonomous Data Guard local and cross-region recovery objectives.

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