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| Section | Weight | Objectives |
|---|---|---|
| Monitor applications with deep visibility into end-user experience | 20% | - Explore the key concepts of Application Performance Monitoring (APM) - Instrument applications for data collection - Examine visualizing and analyzing performance data |
| Respond to cloud resource changes in real-time | 10% | - Respond to Events and integration with OCI services - Analyze the key concepts of Events Service - Determine Event Structure, Event Types and Rules |
| Monitor distributed components of an application stack | 7% | - Identify the key concepts of Stack Monitoring - Analyze discovering resources and monitoring with metrics |
| Identify log data patterns and create visualizations for advanced analytics | 22% | - Explore log ingestion methods for Logging Analytics - Present advanced analytics and features for troubleshooting - Analyze, search, filter and visualize logs - Distinguish the key concepts of Logging Analytics |
| Centrally manage and visualize log data | 16% | - Create Connectors for Log Transitions - Explore managing and searching logs from the entire log estate - Distinguish log categories and enable log collection from sources |
| Monitor cloud environments with metrics and alarms | 18% | - Discuss enabling Metrics for monitoring OCI resources - Explain the key concepts of Monitoring Service - Configure Alarm Definitions using best practices |
| Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
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質問 # 90
Your on-premises private cloud environment consists of virtual machines hosting a set of application servers.
These VMs are currently monitored using a 3rd party monitoring tool for resource metrics such as CPU and Memory utilization. You have created an automation workflow to transform these application servers into Oracle Cloud Infrastructure (OCI) which will deploy a set of new compute instances. There are a few requirements to consider while running this task: Ensure continuous monitoring is enabled, so the current monitored resource metrics are continuously collected and reported; Monitor the completion of Compute Instance deployment during the workflow and notify with email on each execution; Notify with email for any new OCI Object Storage buckets created after the migration workflow. What solution would you recommend to achieve these requirements?
正解：D
解説：
The solution must address continuous monitoring and event-driven notifications:
* D:
* OCI Compute agent on on-premises VMs and OCI instances: Ensures metric continuity (e.g., CPU, memory) across the migration, using Management Agents for both environments.
* Events service: Tracks launchinstance.end for deployment completion and createbucket for new buckets.
* Notifications and Events: Sends email alerts for these events.
* Why not A, B, or C?
* A: Misses on-premises monitoring continuity.
* B: Lacks bucket creation tracking.
* C: Redundant 3rd-party tool use; OCI agents suffice.
D provides end-to-end coverage.
Reference: Management Agents , Events Service
質問 # 91
Which two resources can be monitored by Stack Monitoring? (Choose two.)
正解：A、C
解説：
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
質問 # 92
Which is a valid Log Category name in Oracle Cloud Infrastructure (OCI) Logging Service?
正解：A
解説：
In OCI Logging Service, Log Categories classify logs based on their origin or purpose.
* Custom Logs (D): This is a valid Log Category for logs generated by user applications or services not natively integrated with OCI. Custom Logs are collected using agents, SDKs, or APIs and are user- defined.
* Why not A, B, or C?
* VCN Logs (A): Virtual Cloud Network (VCN) flow logs exist, but "VCN Logs" isn't a formal Log Category; it's a type of service log.
* OCI Agent Logs (B): Agent logs are internal to Management Agents, not a user-facing Log Category.
* System Logs (C): While system logs exist in some contexts, OCI Logging uses specific categories like "Audit Logs" or "Service Logs," not a generic "System Logs."
"Custom Logs" is explicitly supported for user-generated log data.
Reference: Logging Service Concepts
質問 # 93
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 # 94
Which is an example of Log Sources in Logging Analytics?
正解：B
解説：
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
質問 # 95
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