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Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)

A. Use histogram records
B. Use parsed logs search
C. Use specific keywords
D. Use time-picker to limit the volume of logs

Answer: B,D

Explanation:
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.

NEW QUESTION # 29
Which is one of the primary use cases for the Oracle Cloud Infrastructure (OCI) Observability and Management (O&M) Logging Analytics service?

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

Answer: C

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 # 30
In Application Performance Monitoring (APM), a distributed tracing user initiates a request through a browser. What is the first span called?

A. Root span
B. Trace ID
C. Ajax call

Answer: A

Explanation:
In distributed tracing within OCI APM:
Root span (C): The first span in a trace, representing the entry point of a user request (e.g., an HTTP request from a browser). It has no parent span and initiates the chain of subsequent spans across services.
Why not A or B?
Ajax call (A): A type of request, not a span term.
Trace ID (B): A unique identifier for the entire trace, not a span.
The root span is foundational to tracing a request's journey.

NEW QUESTION # 31
Which is an example of Log Sources in Logging Analytics?

A. File, Database, Windows Events System, Syslogs
B. JSON, XML, CSV files
C. Long, Integer, String fields
D. Windows Events, Syslog Listener, and Database SQL parsers

Answer: D

Explanation:
In OCI Logging Analytics, Log Sources are predefined parsers that extract fields from specific types of log data, enabling structured analysis.
Windows Events, Syslog Listener, and Database SQL parsers (B): These are examples of Log Sources in Logging Analytics. Each represents a specific log type with a predefined parser:
Windows Events: Parses event logs from Windows systems (e.g., security, application logs).
Syslog Listener: Handles logs in the Syslog format, common in Unix-based systems or network devices.
Database SQL parsers: Extracts fields from database logs (e.g., Oracle Database audit logs).
These sources come with built-in field mappings and labels for analysis.
Why not A, C, or D?
Long, Integer, String fields (A): These are data types, not Log Sources.
File, Database, Windows Events System, Syslogs (C): While close, this mixes log locations (e.g., File, Database) with source types and isn't a precise match to predefined Log Sources.
JSON, XML, CSV files (D): These are file formats, not Log Sources; Logging Analytics can parse them but they're not predefined sources.
Log Sources streamline log ingestion by providing out-of-the-box parsing for common log types.

NEW QUESTION # 32
Which are the different data sources from where the Application Performance Monitoring (APM) Java agent can collect spans and metrics data?

A. NginX
B. Jaeger or Zipkin
C. WebLogic, Tomcat, or JBoss
D. VMware ESXi

Answer: B

Explanation:
The APM Java Agent collects telemetry from Java applications:
Jaeger or Zipkin (C): These are open-source distributed tracing systems. The Java Agent can integrate with Jaeger- or Zipkin-compatible applications, collecting spans and metrics for APM analysis.
Why not A, B, or D?
NginX (A): A web server; APM uses other agents (e.g., Browser Agent) for such systems.
WebLogic, etc. (B): Application servers, but not direct data sources; the agent collects from the app, not the server type.
VMware ESXi (D): A hypervisor, unrelated to Java tracing.
Jaeger and Zipkin compatibility extends APM's reach.

NEW QUESTION # 33
......

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