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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Alarms and Notifications | 25% | - Alarm configuration, thresholds, and status - Notifications service and integration patterns |
| Topic 2: Logging and Log Analytics | 25% | - Logging Analytics: ingestion, parsing, and analytics - OCI Logging service and log management |
| Topic 3: Application Performance Monitoring and Distributed Tracing | 20% | - APM setup, instrumentation, and end-user monitoring - Distributed tracing, diagnostics, and troubleshooting |
| Topic 4: OCI Monitoring and Metrics | 30% | - Monitoring service concepts and metrics collection - Metric queries, dashboards, and visualization |
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NEW QUESTION # 55
Which two resources can be monitored by Stack Monitoring? (Choose two.)
Answer: B,D
Explanation:
Stack Monitoring tracks application stack components:
* WebLogic Servers (B): Monitors performance and health of WebLogic instances.
* Oracle External Databases (C): Tracks on-premises or cloud Oracle databases outside OCI's native DBaaS.
* Why not A or D?
* Object Storage Buckets (A): Not supported by Stack Monitoring; use Logging instead.
* Virtual Cloud Networks (D): Network monitoring is separate (e.g., VCN Flow Logs).
These align with Stack Monitoring's focus on application stacks.
Reference: Stack Monitoring Resources
NEW QUESTION # 56
You are a cloud administrator tasked with creating a dashboard that displays the distribution of a field's values, such as the number of occurrences of each HTTP error code. Which OCI Logging Analytics visualization should you use?
Answer: A
Explanation:
A pie chart is suitable for displaying how the total number of log records is distributed among the distinct values of a selected field. For example, a query can group records by HTTP status code and calculate the count associated with each code. The pie chart displays each status code as a segment, making the relative proportion of errors such as 400, 401, 404, or 500 easy to compare. A single-number tile presents only one aggregated value and cannot communicate the distribution among multiple codes. A table can list the values and counts but provides less immediate visual comparison. Cluster visualization is a machine learning-based capability that groups log records according to their underlying patterns or shapes rather than simply presenting a categorical field distribution. Therefore, the pie chart is the best answer. Oracle documentation
NEW QUESTION # 57
Which is an example of Log Sources in Logging Analytics?
Answer: B
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.
Reference: Logging Analytics Log Sources
NEW QUESTION # 58
Which are the different data sources from where the Application Performance Monitoring (APM) Java agent can collect spans and metrics data?
Answer: A
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.
Reference: APM Java Agent
NEW QUESTION # 59
You are configuring OCI Logging to collect logs from custom applications running on Compute instances.
What is the primary purpose of creating an Agent Configuration?
Answer: C
Explanation:
An OCI Logging Agent Configuration centrally defines how custom logs are collected across a fleet of supported Compute instances. It identifies the applicable hosts through user groups or dynamic groups, specifies the log inputs to monitor-such as file paths or Windows event channels-and selects the destination custom log and log group. It can also define parsing options and enable operational metrics for monitoring the Unified Monitoring Agent. The configuration does not allocate Object Storage or other storage capacity when log volume grows. Although OCI protects log data using platform security controls, the primary purpose of the Agent Configuration is not to perform client-side encryption. Its main role is to connect selected hosts, input definitions, parsing behavior, and log destinations in one centrally managed collection policy. Oracle documentation
NEW QUESTION # 60
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