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Oracle 1Z0-1111-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Define the Pillars of Observability: This section of the exam measures the skills of Cloud Architects and Site Reliability Engineers (SREs) in understanding the three pillars of observability: logs, metrics, and traces. It explains how these components provide insights into system health and performance, enabling effective monitoring and troubleshooting in distributed cloud environments.
Topic 2	Centrally Manage and Visualize Log Data: This section assesses the abilities of Logging Analysts and Security Engineers in managing logs across an entire IT environment. It includes enabling log collection, creating connectors for log transitions, identifying log patterns, and visualizing logs for advanced analytics using OCI Logging Service.
Topic 3	Monitor Cloud Environments with Metrics and Alarms: This section tests Oracle Cloud Operators' knowledge of configuring OCI Monitoring Service. It includes enabling metrics for resource monitoring, setting up alarms based on best practices, and responding to real-time changes in cloud environments to ensure system reliability.
Topic 4	Summarize OCI Observability and Management Services: This section evaluates the expertise of DevOps Engineers and IT Operations Managers in utilizing Oracle Cloud Infrastructure (OCI) Observability and Management services. It covers tools for monitoring, auditing, and managing cloud resources, emphasizing automation and machine learning-driven insights to optimize IT operations.
Topic 5	Monitor Applications with Deep Visibility into End-User Experience: This domain tests the knowledge of Application Performance Engineers and DevOps Specialists in using OCI Application Performance Monitoring (APM). It includes instrumenting applications for data collection, analyzing performance metrics, visualizing distributed application components, and diagnosing issues across multi-tier architectures.
Topic 6	Distinguish the Key Concepts of Logging Analytics: This section evaluates the expertise of Data Analysts and Troubleshooting Specialists in using OCI Logging Analytics. It covers log ingestion methods, searching and filtering logs, performing advanced analytics, and leveraging machine learning-powered insights for proactive issue resolution.
Topic 7	Monitor Distributed Components of an Application Stack: This section measures the skills of Stack Monitoring Specialists and Cloud Administrators in using OCI Stack Monitoring. It covers discovering resources within application stacks, monitoring metrics across distributed components, and ensuring optimal performance through effective resource management.

Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
When would you use a vantage point in Application Performance Monitoring (APM)?

A. Application Insights
B. Java Management
C. Synthetic Monitoring
D. Distributed Tracing

Answer: C

Explanation:
In APM, a vantage point is used in:
Synthetic Monitoring (D): Runs tests from specific locations (vantage points) to monitor web application or API availability and performance globally.
Why not A, B, or C?
Java Management (A): Unrelated to vantage points.
Distributed Tracing (B): Tracks internal request flows, not external tests.
Application Insights (C): Not a formal APM feature; vague term.
Vantage points simulate user access from different regions.

NEW QUESTION # 35
What two APM agents can Application Performance Monitoring use to collect data? (Choose two.)

A. Java Agent
B. Cloud Agent
C. Browser Agent
D. Management Agent

Answer: A,C

Explanation:
OCI APM uses specific agents for data collection:
Java Agent (B): Attaches to Java applications to collect traces, metrics, and errors for APM.
Browser Agent (D): A JavaScript snippet embedded in web pages to collect Real User Monitoring (RUM) data (e.g., page load times).
Why not A or C?
Management Agent (A): Used for Stack Monitoring/Operations Insights, not APM.
Cloud Agent (C): Monitors compute instances, not an APM-specific agent.
These agents target application and user experience monitoring.

NEW QUESTION # 36
Which two future resource usages are identified by Exadata Warehouse Insights custom analytics under Operations Insights? (Choose two.)

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

Answer: A,B

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 # 37
Which is one of the primary use cases for the Oracle Cloud Infrastructure (OCI) Observability and Management (O&M) Logging Analytics service?

A. Centralize and relocate any log based on a subscription model
B. Monitor, aggregate, index, and analyze log data
C. Create OCI resources automatically based on log events and reports

Answer: B

Explanation:
Logging Analytics is a core O&M service:
Monitor, aggregate, index, and analyze log data (A): Collects logs from OCI and external sources, indexes them for search, and provides analytics (e.g., clustering) to monitor and troubleshoot systems.
Why not B or C?
B: Log centralization occurs, but "subscription model" isn't a feature.
C: Resource creation is an Events Service use case, not Logging Analytics.
This is its primary observability role.

NEW QUESTION # 38
Which two components are optional while creating the Monitoring Query Language (MQL) expressions in the Oracle Cloud Infrastructure (OCI) Monitoring service? (Choose two.)

A. Previous
B. Statistic
C. Grouping Function
D. Dimensions
E. Metric
F. Interval

Answer: B,C

Explanation:
MQL expressions in OCI Monitoring retrieve and process metric data. Optional components include:
Statistic (B): Specifies how to summarize data (e.g., avg, max). If omitted, raw data is returned.
Grouping Function (D): Aggregates data across metric streams (e.g., by resourceId). It's optional if no grouping is needed.
Why not A, C, E, or F?
Interval (A): Defines the time window; defaults apply if omitted, but it's typically required for meaningful queries.
Previous (C): Not a standard MQL component (possibly a typo).
Metric (E): Mandatory to specify what's being queried.
Dimensions (F): Filters data; required if specific streams are targeted, though optional in basic queries.
Statistic and Grouping Function enhance flexibility but aren't mandatory.

NEW QUESTION # 39
......

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