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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Centrally manage and visualize log data | 16% | - Create Connectors for Log Transitions - Distinguish log categories and enable log collection from sources - Explore managing and searching logs from the entire log estate |
| Topic 2: Respond to cloud resource changes in real-time | 10% | - Respond to Events and integration with OCI services - Determine Event Structure, Event Types and Rules - Analyze the key concepts of Events Service |
| Topic 3: Monitor applications with deep visibility into end-user experience | 20% | - Examine visualizing and analyzing performance data - Explore the key concepts of Application Performance Monitoring (APM) - Instrument applications for data collection |
| Topic 4: Monitor distributed components of an application stack | 7% | - Analyze discovering resources and monitoring with metrics - Identify the key concepts of Stack Monitoring |
| Topic 5: Monitor cloud environments with metrics and alarms | 18% | - Configure Alarm Definitions using best practices - Discuss enabling Metrics for monitoring OCI resources - Explain the key concepts of Monitoring Service |
| Topic 6: Identify log data patterns and create visualizations for advanced analytics | 22% | - Present advanced analytics and features for troubleshooting - Distinguish the key concepts of Logging Analytics - Analyze, search, filter and visualize logs - Explore log ingestion methods for Logging Analytics |
| Topic 7: Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
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質問 # 31
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?
正解：C
解説：
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.
Reference: Logging Analytics Cluster
質問 # 32
Which two are use cases of Oracle Cloud Infrastructure (OCI) Events Service? (Choose two.)
正解：B、C
解説：
The OCI Events Service enables event-driven automation by reacting to changes in OCI resources. Two valid use cases are:
* Process files when they are uploaded in an Object Storage bucket (A): You can create an event rule to trigger an action (e.g., invoking an Oracle Function) when an object is created (com.oraclecloud.
objectstorage.createobject). The function could process the file (e.g., image resizing).
* Perform cleanup tasks when an OCI resource is terminated (B): An event rule can detect resource termination (e.g., com.oraclecloud.computeapi.terminateinstance.end) and trigger a function to clean up associated resources (e.g., delete volumes).
* Why not C or D?
* Migrate Events (C): Events Service doesn't "migrate" events; it triggers actions. Migration is more aligned with Service Connector Hub.
* Configuration management (D): This is handled by services like Resource Manager or Ansible, not Events Service.
These use cases showcase the service's ability to automate workflows based on resource state changes.
Reference: Events Service Use Cases
質問 # 33
Which TWO Observability and Management (O & M) services are supported by Management Agent?
(Choose two.)
正解：A、B
解説：
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.
Reference: Management Agent Supported Services
質問 # 34
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?
正解：C
解説：
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.
Repeat Notification ensures ongoing alerts.
Reference: Creating Alarms
質問 # 35
Which Management Agent group allows the agent to upload data to the discovery service?
正解：D
解説：
Management Agents require group membership and policies to interact with OCI services:
* AgentUsersGrp (A): A predefined dynamic group for all Management Agents in a tenancy. With an attached policy granting upload permissions (e.g., allow dynamic-group AgentUsersGrp to use discovery-service-family in tenancy), agents can send data to the discovery service for resource detection.
* Why not B, C, or D?
* B and C: Specific to Stack Monitoring roles, not general agent uploads.
* D: A generic name, not a predefined OCI group.
AgentUsersGrp is the standard for this purpose.
Reference: Management Agent Policies
質問 # 36
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