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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Monitoring and Metrics | 30% | - Metric queries, dashboards, and visualization - Monitoring service concepts and metrics collection |
| Topic 2: Logging and Log Analytics | 25% | - Logging Analytics: ingestion, parsing, and analytics - OCI Logging service and log management |
| Topic 3: Alarms and Notifications | 25% | - Alarm configuration, thresholds, and status - Notifications service and integration patterns |
| Topic 4: Application Performance Monitoring and Distributed Tracing | 20% | - Distributed tracing, diagnostics, and troubleshooting - APM setup, instrumentation, and end-user monitoring |
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質問 # 28
What is the function of OCI Management Agent?
正解：A
解説：
OCI Management Agent is installed on a host and provides a framework through which supported OCI service plug-ins collect telemetry from monitored data sources. Depending on the deployed plug-ins and configuration, the agent can collect logs, metrics, configuration information, status information, and other management data and upload it securely to the corresponding OCI service. The Management Agent service also manages the agents, their plug-ins, registration, and lifecycle. Distributed trace analysis is primarily performed by OCI Application Performance Monitoring and its APM agents. Cost and usage optimization belongs to cost-management and analytics capabilities, while responding to resource state changes is the role of OCI Events and its configured actions. Therefore, enabling communication and data collection between managed targets and supported OCI services is the Management Agent's fundamental function. Oracle documentation
質問 # 29
Your team has been tasked with debugging a Cloud Native application developed using the following Oracle Cloud Infrastructure (OCI) services: Object Storage, Events, Functions, API Gateway, and Autonomous Database. Which of these is NOT a valid option for troubleshooting issues in OCI?
正解：E
解説：
Troubleshooting a cloud-native app leverages OCI observability tools:
* Invalid: Leverage OCI Cloud Guard to extract and visualize debug logs (D): Cloud Guard is a security posture management and threat detection service, not designed for extracting or visualizing application debug logs.
* Why A, B, C, and E are valid:
* A: Monitoring provides service metrics (e.g., Function invocation latency).
* B: Service Connector moves logs to Logging Analytics for analysis.
* C: APM traces Functions performance issues.
* E: Logging Service collects app logs directly.
Cloud Guard focuses on security, not debugging.
Reference: Cloud Guard
質問 # 30
What happens in Stack Monitoring after Management Agents are set up and resources are discovered?
正解：D
解説：
In OCI Stack Monitoring, once Management Agents are deployed and resources (e.g., databases, applications) are discovered, the immediate next step is the collection of metric data.
* Metric data is immediately collected (A): Management Agents are lightweight processes that continuously collect performance and health metrics from discovered resources (e.g., CPU usage, memory utilization) and send them to OCI services like Monitoring or Stack Monitoring. This data becomes available for visualization and analysis right after discovery.
* Why not B, C, or D?
* Alarm rules (B): Alarms are configured separately in the OCI Monitoring service and only trigger after metric data is collected and thresholds are breached-not an immediate post-discovery action.
* Resource discovery (C): Discovery happens before this stage, as the question assumes resources are already discovered. Agents don't rediscover resources post-setup.
* Notifications (D): Notifications require separate configuration (e.g., via the Notifications service) and are not an automatic outcome of agent setup and discovery.
This aligns with Stack Monitoring's purpose of providing real-time visibility into resource performance.
Reference: Stack Monitoring Overview , Management Agent
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：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 # 32
Which observability pillar is best suited for identifying performance bottlenecks across a distributed system by tracking request flows from end to end?
正解：B
解説：
A trace represents the complete flow of a request as it passes through the components of a distributed system during a specific period. It consists of related spans, with each span representing an operation or logical unit of work and recording information such as its name, start time, and duration. This structure allows engineers to follow a request across services and determine where excessive latency, failures, or other bottlenecks occur.
Metrics summarize numerical behavior over time but do not ordinarily show the complete path of an individual request. Logs provide detailed event records, while events report state changes or occurrences.
Those signals remain valuable, but distributed tracing is specifically designed to visualize and diagnose end- to-end request execution across interconnected services. Oracle documentation
質問 # 33
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