LATEST 1Z0-1111-25 TEST CAMP - 100% PASS 1Z0-1111-25 - FIRST-GRADE ORACLE CLOUD INFRASTRUCTURE 2025 OBSERVABILITY PROFESSIONAL CERTIFICATION

Latest 1Z0-1111-25 Test Camp - 100% Pass 1Z0-1111-25 - First-grade Oracle Cloud Infrastructure 2025 Observability Professional Certification

Latest 1Z0-1111-25 Test Camp - 100% Pass 1Z0-1111-25 - First-grade Oracle Cloud Infrastructure 2025 Observability Professional Certification

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Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
What happens in Stack Monitoring after Management Agents are set up and resources are discovered?

  • A. OCI Notifications send email notifications
  • B. Alarm rules will trigger when resources are down or performance thresholds are crossed
  • C. Management Agents discover resources that are running locally on the instance
  • D. Metric data is immediately collected

Answer: D

Explanation:
In OCI Stack Monitoring, once Management Agents are deployed and resources (e.g., databases, applications) are discovered, the immediate next step is the collection of metric data.
Metric data is immediately collected (A): Management Agents are lightweight processes that continuously collect performance and health metrics from discovered resources (e.g., CPU usage, memory utilization) and send them to OCI services like Monitoring or Stack Monitoring. This data becomes available for visualization and analysis right after discovery.
Why not B, C, or D?
Alarm rules (B): Alarms are configured separately in the OCI Monitoring service and only trigger after metric data is collected and thresholds are breached-not an immediate post-discovery action.
Resource discovery (C): Discovery happens before this stage, as the question assumes resources are already discovered. Agents don't rediscover resources post-setup.
Notifications (D): Notifications require separate configuration (e.g., via the Notifications service) and are not an automatic outcome of agent setup and discovery.
This aligns with Stack Monitoring's purpose of providing real-time visibility into resource performance.


NEW QUESTION # 58
You are part of a team that manages a set of workload instances running in an on-premises environment. The Architect team is tasked with designing and configuring Oracle Cloud Infrastructure (OCI) Logging service to collect logs from these instances. There is a requirement to archive Info-level logging data of these instances into OCI Object Storage. Which two features of OCI can help you achieve this? (Choose two.)

  • A. Agent Configuration
  • B. ObjectCollection Rule
  • C. Service Connectors
  • D. Cloud Agent Plugin Grouping Function

Answer: B,C

Explanation:
To collect logs from on-premises instances and archive Info-level logs in OCI Object Storage, you need tools for log ingestion and data movement:
Service Connectors (A): This feature enables data transfer from OCI Logging (source) to Object Storage (target). You can configure a service connector with a filter (e.g., log level = Info) to archive only Info-level logs.
ObjectCollection Rule (D): Part of Logging Analytics, this rule collects logs from Object Storage buckets into Logging Analytics for analysis. If logs are first written to Object Storage by an agent, this rule ensures continuous ingestion.
Why not B or C?
Agent Configuration (B): Used to set up Management Agents but doesn't handle archiving to Object Storage.
Cloud Agent Plugin Grouping Function (C): This is not a valid OCI feature.
The workflow involves agents sending logs to Logging, Service Connectors filtering and moving them to Object Storage, and ObjectCollection Rules enabling further analysis.


NEW QUESTION # 59
Choose two FluentD scenarios that apply when using continuous log collection with client-side processing. (Choose two.)

  • A. Log Source
  • B. Managing apps/services which push logs to Object Storage
  • C. Comprehensive monitoring for OKE/Kubernetes
  • D. Monitoring systems that are not currently supported by Management Agent

Answer: B,C

Explanation:
FluentD is an open-source data collector used for continuous log collection with client-side processing in OCI Logging. Two applicable scenarios are:
Managing apps/services which push logs to Object Storage (A): FluentD can be configured to collect logs from applications or services (e.g., Oracle Functions) that write logs to Object Storage buckets. It processes these logs client-side and forwards them to OCI Logging or Logging Analytics.
Comprehensive monitoring for OKE/Kubernetes (B): FluentD is widely used in Kubernetes environments like Oracle Container Engine for Kubernetes (OKE) to collect logs from pods, containers, and nodes. It processes these logs locally before sending them to OCI services for analysis.
Why not C or D?
Monitoring unsupported systems (C): While possible, this is not a primary FluentD scenario in OCI-it's more about extending Management Agent capabilities.
Log Source (D): This is a component of Logging Analytics, not a FluentD scenario.
FluentD's flexibility makes it ideal for these use cases in OCI's observability ecosystem.


NEW QUESTION # 60
Which two future resource usages are identified by Exadata Warehouse Insights custom analytics under Operations Insights? (Choose two.)

  • A. Memory
  • B. AIOps
  • C. Network usage
  • D. CPU

Answer: A,D

Explanation:
Exadata Warehouse Insights in OCI Operations Insights provides advanced analytics to forecast resource usage for Exadata systems.
Memory (A): Tracks and predicts memory utilization based on historical trends, aiding capacity planning.
CPU (D): Forecasts CPU usage, helping identify potential bottlenecks or over-provisioning.
Why not B or C?
Network usage (B): While monitored, it's not a primary focus of Exadata Warehouse Insights' future usage predictions.
AIOps (C): This is a methodology, not a resource usage metric.
These forecasts leverage historical data and what-if analysis for proactive management.


NEW QUESTION # 61
Which response contains rich information to process for analytics?

  • A. Database Audit Logs
  • B. Entity types
  • C. Logging Analytic Entities
  • D. Log Sources

Answer: A

Explanation:
For analytics, the data source must provide detailed, actionable information.
Database Audit Logs (C): These logs contain rich data like user actions, SQL queries, timestamps, and security events, making them ideal for performance, security, and compliance analysis in Logging Analytics.
Why not A, B, or D?
Entity types (A): These are metadata definitions, not data for analytics.
Log Sources (B): These are configurations for log parsing, not the logs themselves.
Logging Analytic Entities (D): Entities are resource representations, not the data content.
Database Audit Logs offer the depth needed for meaningful insights.


NEW QUESTION # 62
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