Selected 1z0-931-25 Oracle Cloud Infrastructure Questions Answers

Setting up notifications for scheduled shutdowns leverages OCI Events:

Correct Answer (A): Creating a rule for the 'Autonomous Database STOP END' event in OCI Events triggers a notification (e.g., email via Notification Service) when the shutdown completes. This is the recommended method for tracking lifecycle events in Autonomous Database.

Incorrect Options:

B: There is no specific “Shutdown” metric in OCI Metrics; alarms monitor performance metrics, not lifecycle events like shutdowns.

C: Database triggers like BEFORE SHUTDOWN are not supported in Autonomous Database due to its managed nature, and they wouldn’t integrate with OCI notifications.

Disabling Autonomous Data Guard affects the standby database:

Correct Answer (A): “The standby database is terminated” is true. When Data Guard is disabled via the OCI Console or API, the standby database is deleted, ending its role as a replica. This is a permanent action requiring re-enablement to recreate a standby.

Incorrect Options:

B: The standby doesn’t become read-write; it’s removed entirely.

C: It doesn’t remain read-only; termination stops all operations.

The scenario requires a low-maintenance, high-performance database for an OLTP web application with occasional reporting. The correct answer is:

ATP using 'tpurgent' and 'high' TNS services to separate connection types (A):Autonomous Transaction Processing (ATP) is ideal here. It’s a fully managed database optimized for OLTP workloads (e.g., scheduling service requests) with zero maintenance overhead—Oracle handles patching, backups, and tuning. ATP supports multiple connection services:

'tpurgent':Prioritizes low-latency, time-critical transactions (e.g., customer scheduling requests), ensuring fast response times for the web app.

'high':Suited for high-concurrency or reporting queries, allowing operational reports to run during non-peak hours without impacting the OLTP workload.By using these predefined services, the architecture separates transactional and reporting workloads within the same database, meeting performance needs without additional administration. For example, the web app connects via tpurgent for real-time updates, while a reporting tool uses high for batch queries at night, leveraging ATP’s auto-scaling if needed.

The incorrect options are:

Since the application needs to be highly available, it should be deployed on a Kubernetes Cluster (B):This misinterprets the question—it’s about the database service,not the application deployment. Kubernetes is for container orchestration, not a database solution, and adds complexity counter to the low-maintenance goal.

It is best to build a separate data warehouse, and move the OLTP data on a nightly basis (C):While a separate Autonomous Data Warehouse (ADW) could handle reporting, it requires data movement (e.g., via ETL), increasing admin effort and complexity, which the DBA Manager wants to avoid. ATP can handle both workloads with proper service separation.

ADW since operational reporting is a higher priority in this scenario (D):ADW is optimized for analytics, not OLTP. The web app’s transactional needs are primary, with reporting secondary and occasional, making ATP more suitable.

ATP’s self-managing nature and service flexibility make it the best fit.

Scaling OCPUs in Autonomous Database is designed to be seamless. The two true statements are:

Scaling can happen while there are active transactions in the database (B):ADB supports online scaling, meaning you can increase or decrease OCPUs (e.g., from 2 to 4) via the OCI console or CLI (e.g., oci db autonomous-database update --cpu-core-count 4) without stopping the database. Active transactions (e.g., INSERT INTO orders VALUES (...)) continue running during this process. Oracle’s architecture ensures the database remains available, adjusting resources in the background. For example, a web app processing orders won’t notice the scaling operation starting at 10:00 AM.

Active transactions continue running unaffected (C):During scaling, existing transactions are not interrupted, terminated, or paused. They complete normally, with Oracle managing resource allocation transparently (e.g., shifting CPU usage without killing sessions). For instance, a long-running UPDATE statement started before scaling finishes successfully, leveraging the database’s high-availability design. The status shows “SCALING IN PROGRESS,” but users experience no downtime.

The incorrect options are:

Active transactions are terminated and rolled back (A):False. Scaling is non-disruptive; transactions aren’t killed or rolled back, preserving data integrity and user experience. Termination only occurs during explicit stops or failures, not scaling.

Active transactions are paused (D):False. There’s no pausing mechanism during scaling; transactions run continuously, as pausing would disrupt OLTP or analytical workloads, countering ADB’s autonomous promise.

This online scaling capability is a key benefit, ensuring uninterrupted service.