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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
| Topic 2: Monitor distributed components of an application stack | 7% | - Discover and monitor distributed application components - Use Stack Monitoring - Analyze infrastructure and application metrics |
| Topic 3: Monitor applications with deep visibility into end-user experience | 20% | - Configure application instrumentation and data collection - Analyze application performance and end-user experience - Use Application Performance Monitoring |
| Topic 4: Identify log data patterns and create visualizations for advanced analytics | 22% | - Use Logging Analytics for log ingestion and analysis - Search, filter and visualize log data - Perform advanced log analytics and troubleshooting |
| Topic 5: 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 6: Monitor cloud environments with metrics and alarms | 18% | - Discuss enabling Metrics for monitoring OCI resources - Configure Alarm Definitions using best practices - Explain the key concepts of Monitoring Service |
| Topic 7: Centrally manage and visualize log data | 16% | - Configure logging and log connectors - Manage logs using OCI Logging - Collect, search and analyze log data |
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질문 # 77
A cloud operations team is using LoganAI to analyze large volumes of log data ingested into OCI Log Analytics. Which two LoganAI capabilities support this analysis?
정답：A,B
설명：
LoganAI is the artificial intelligence capability integrated with Oracle Log Analytics. Its Explain features generate concise summaries and interpretations for individual log records, multiple records, clusters, issues, outliers, and supported charts. This helps administrators understand large datasets without manually reading every log entry. LoganAI also generates relevant, context-aware follow-up questions and permits users to ask their own questions, supporting an interactive investigation of patterns, anomalies, and potential problems.
The capability can explain complex log information in accessible language and accelerate troubleshooting, although Oracle recommends validating AI-generated conclusions before making operational decisions.
LoganAI does not migrate logs between regions and does not automatically provision sources for log ingestion. Those are separate administrative and data-management activities. Therefore, summaries and interactive follow-up questions are the two valid capabilities. Oracle documentation
질문 # 78
Which of the following TWO are stored in a Log Source of Logging Analytics? (Choose two.)
정답：B,D
설명：
A Log Source in Logging Analytics defines how logs are collected and processed:
* Which Parsers to use (A): Specifies the parsers (e.g., Syslog, JSON) that extract fields from logs, enabling structured analysis.
* Where to find Logs (C): Defines the log location (e.g., file path /var/log/messages, database connection string), directing the collection process.
* Why not B or D?
* Where to store Log data (B): Storage is managed by Logging Analytics, not defined in the Source.
* Which Management Agents to use (D): Agents are associated with Entities, not specified in the Source.
These elements configure log ingestion effectively.
Reference: Logging Analytics Log Sources
질문 # 79
Which statement is true about OCI Logging versus OCI Logging Analytics?
정답：B
설명：
OCI Logging is a fully managed service that centralizes service logs, custom logs, and other logs generated by OCI resources. It supports log enablement, management, searching, and integration with services such as Service Connector Hub, Functions, Streaming, Monitoring, and Notifications. OCI Logging Analytics provides broader analytical capabilities for logs originating from OCI, on-premises infrastructure, and multicloud environments. It can index, enrich, aggregate, correlate, visualize, and monitor log data while applying machine learning techniques for clustering, anomaly detection, and operational analysis. Logging Analytics is therefore not the same service as OCI Logging, and OCI Logging is not limited to archiving.
Option C most accurately distinguishes the operational log-management service from the advanced log- analysis platform. Oracle documentation
질문 # 80
How does Application Performance Monitoring track all related spans for a single user request?
정답：C
설명：
APM tracks request flows using:
* Using Trace ID (A): A unique identifier assigned to a trace (collection of spans) for a single user request. Propagated via HTTP headers, it links all spans across services.
* Why not B or C?
* User ID (B): Identifies users, not request flows.
* Application Name (C): Too broad; doesn't correlate specific requests.
Trace ID ensures end-to-end visibility in distributed systems.
Reference: Traces and Spans
질문 # 81
Which is one of the primary use cases for the Oracle Cloud Infrastructure (OCI) Observability and Management (O & M) Logging Analytics service?
정답：C
설명：
Logging Analytics is a core O & M service:
* Monitor, aggregate, index, and analyze log data (A): Collects logs from OCI and external sources, indexes them for search, and provides analytics (e.g., clustering) to monitor and troubleshoot systems.
* Why not B or C?
* B: Log centralization occurs, but "subscription model" isn't a feature.
* C: Resource creation is an Events Service use case, not Logging Analytics.
This is its primary observability role.
Reference: Logging Analytics Overview
질문 # 82
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