You have configured an Alarm Definition in the Oracle Cloud Infrastructure (OCI) Monitoring service to send notifications through email. The alarm should resend notifications at specified intervals if the alarm continues to be in the firing state. A subscriber in the Notifications Topic complains about not receiving multiple emails upon failures. Which of the following could be the possible cause of this issue?
Answer : B
For repeated notifications in OCI Monitoring:
Repeat notification was not enabled while creating the Alarm (B): The ''Repeat Notification'' option must be enabled in the alarm definition, specifying an interval (e.g., every 5 minutes). If disabled, only one notification is sent when the alarm fires, explaining the subscriber's issue.
Why not A, C, or D?
A: False; OCI supports repeat notifications if configured.
C: Trigger condition is mandatory for any alarm; this isn't the issue.
D: Notification frequency isn't tied to metric streams, only the repeat setting.
Which TWO Observability and Management (O&M) services are supported by Management Agent? (Choose two.)
Answer : B, C
Management Agents collect and send data to OCI services:
Logging Analytics (B): Agents gather log data from various sources (e.g., files, databases) and send it to Logging Analytics for indexing and analysis.
Enterprise Manager (C): Agents integrate with Oracle Enterprise Manager, enabling monitoring of on-premises or cloud targets within OCI.
Why not A or D?
Application Performance Management (A): Uses Java and Browser Agents, not Management Agents.
Database Management (D): Leverages agents indirectly via other services, not a direct target.
These services leverage Management Agents for observability.
What is the correct logging CLI syntax for the log search with a query for REST call responses having status code 400, within the web Log Group and the application Log?
Answer : B
The correct OCI CLI syntax for searching logs must include the compartment, log group, log, and query:
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?
Answer : A
In Logging Analytics, ML-driven visualizations aid log analysis:
Cluster (A): Uses machine learning to group logs by structural similarity (''shape''), reducing noise and highlighting patterns or anomalies in large datasets.
Why not B or C?
Summary Table (B): Aggregates data tabularly, not ML-based or shape-focused.
Word Cloud (C): Displays word frequency, not structural grouping.
Cluster is ideal for large-scale log pattern recognition.
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)
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.
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