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| Section | Weight | Objectives |
|---|---|---|
| OCI Monitoring and Metrics | 30% | - Metric queries, dashboards, and visualization - Monitoring service concepts and metrics collection |
| Application Performance Monitoring and Distributed Tracing | 20% | - APM setup, instrumentation, and end-user monitoring - Distributed tracing, diagnostics, and troubleshooting |
| Logging and Log Analytics | 25% | - Logging Analytics: ingestion, parsing, and analytics - OCI Logging service and log management |
| Alarms and Notifications | 25% | - Alarm configuration, thresholds, and status - Notifications service and integration patterns |
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質問 # 76
As an IT administrator, you need to set up continuous and automatic log ingestion from an OCI compute instance to OCI Logging Analytics. Which method is recommended to meet this requirement?
正解：C
解説：
OCI Management Agent is the recommended mechanism for continuously collecting logs stored on a compute instance or another host. The agent runs on the log-emitting host and uses the Log Analytics plug-in to collect supported file, syslog, database, Windows event, REST API, and other log types. After the relevant entity, source, parser, and source-entity association are configured, the agent automatically sends new log records to Logging Analytics. Manual upload is intended for one-time or historical ingestion rather than continuous collection. The LogEvents API requires custom integration, while Service Connector is primarily used to ingest logs generated by OCI services through OCI Logging. Therefore, enabling OCI Management Agent provides the required continuous and automatic host-based ingestion workflow. Oracle documentation
質問 # 77
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?
正解：B
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 # 78
Which TWO items describe the capabilities of the Oracle Cloud Infrastructure (OCI) Database Management Service? (Choose two.)
正解：B、C
解説：
The Database Management Service enhances database oversight:
* Monitor database performance (B): Provides tools like Performance Hub to track real-time and historical metrics (e.g., CPU, I/O).
* Perform management tasks across a group of databases (C): Database Fleet Management enables bulk operations (e.g., configuration checks, SQL execution) across multiple databases.
* Why not A or D?
* A: Log viewing is via Logging Service, not a core DM feature.
* D: SQL analysis is in Operations Insights, not DM directly.
These capabilities streamline database administration.
Reference: Database Management
質問 # 79
Which Management Agent group allows the agent to upload data to the discovery service?
正解：A
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 # 80
Which are the different data sources from where the Application Performance Monitoring (APM) Java agent can collect spans and metrics data?
正解：B
解説：
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.
Reference: APM Java Agent
質問 # 81
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