Oracle 1Z0-1111-25 Exam Dumps

Get All Oracle Cloud Infrastructure 2025 Observability Professional Exam Questions with Validated Answers

1Z0-1111-25 Pack
Vendor: Oracle
Exam Code: 1Z0-1111-25
Exam Name: Oracle Cloud Infrastructure 2025 Observability Professional
Exam Questions: 61
Last Updated: November 21, 2025
Related Certifications: Oracle Cloud , Oracle Cloud Infrastructure
Exam Tags: Intermediate Level Oracle Cloud Architects
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Free Oracle 1Z0-1111-25 Exam Actual Questions

Question No. 1

When would you use a vantage point in Application Performance Monitoring (APM)?

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Correct Answer: D

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.


Question No. 2

Which response contains rich information to process for analytics?

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Correct Answer: C

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.


Question No. 3

There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)

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Correct Answer: C, D

Reducing noise in Logging Analytics improves log analysis focus:

Use parsed logs search (C): Searches based on extracted fields (e.g., severity=ERROR) filter out irrelevant logs, targeting specific issues.

Use time-picker to limit the volume of logs (D): Narrows the time range (e.g., last hour), reducing the dataset to relevant periods.

Why not A or B?

Histogram records (A): Visualizes data distribution, not a noise reduction method.

Specific keywords (B): Useful but less precise than parsed fields; raw text search isn't emphasized in Logging Analytics.

These methods enhance signal-to-noise ratio.


Question No. 4

Which of the following statements is NOT valid regarding Management Agent Cloud Service?

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Correct Answer: A

The Management Agent Cloud Service collects and transports data from resources to OCI services. Let's evaluate:

Invalid statement: Can only transport data into AWS or GCS (A): This is false. Management Agents transport data to OCI services (e.g., Logging Analytics, Monitoring) or custom OCI endpoints, not AWS or Google Cloud Storage (GCS).

Why B, C, and D are valid:

Self-monitored (B): Agents monitor their own health and report to OCI.

Transports to OCI services (C): Supports Logging Analytics, Monitoring, and custom OCI endpoints.

On-demand operations (D): Allows tasks like metric collection or log uploads on demand.

Management Agents are OCI-centric, not limited to external clouds.


Question No. 5

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

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Correct Answer: A, D

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.


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