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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
| Topic 3: 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 4: Identify log data patterns and create visualizations for advanced analytics | 22% | - Search, filter and visualize log data - Perform advanced log analytics and troubleshooting - Use Logging Analytics for log ingestion and analysis |
| Topic 5: Centrally manage and visualize log data | 16% | - Collect, search and analyze log data - Configure logging and log connectors - Manage logs using OCI Logging |
| Topic 6: Respond to cloud resource changes in real-time | 10% | - Determine Event Structure, Event Types and Rules - Analyze the key concepts of Events Service - Respond to Events and integration with OCI services |
| Topic 7: Monitor distributed components of an application stack | 7% | - Discover and monitor distributed application components - Use Stack Monitoring - Analyze infrastructure and application metrics |
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NEW QUESTION # 84
What are the TWO benefits of Observability Lakehouse in Operations Insights? (Choose two.)
Answer: C,D
Explanation:
The Observability Lakehouse in Operations Insights is a data repository for operational analytics:
* Enables custom analytics (B): Supports trending (e.g., usage patterns), forecasting (e.g., resource needs), capacity planning, and workload profiling using advanced analytical tools, enhancing resource optimization.
* Allows Oracle Enterprise Manager's data (D): Integrates operational data from Enterprise Manager (e.
g., database metrics) for use cases like performance analysis and anomaly detection.
* Why not A or C?
* Statistical analysis of AI data (A): Too vague; Lakehouse focuses on operational data, not AI- specific stats.
* Identifies future resource usage (C): Partial benefit of B, but not a standalone feature.
These capabilities improve operational decision-making.
Reference: Operations Insights Observability Lakehouse
NEW QUESTION # 85
What two APM agents can Application Performance Monitoring use to collect data? (Choose two.)
Answer: B,D
Explanation:
OCI APM uses specific agents for data collection:
* Java Agent (B): Attaches to Java applications to collect traces, metrics, and errors for APM.
* Browser Agent (D): A JavaScript snippet embedded in web pages to collect Real User Monitoring (RUM) data (e.g., page load times).
* Why not A or C?
* Management Agent (A): Used for Stack Monitoring/Operations Insights, not APM.
* Cloud Agent (C): Monitors compute instances, not an APM-specific agent.
These agents target application and user experience monitoring.
Reference: APM Agents
NEW QUESTION # 86
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?
Answer: A
Explanation:
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
NEW QUESTION # 87
What are the TWO SQL Warehouse categories to analyze SQL Performance in Operations Insights? (Choose two.)
Answer: B,C
Explanation:
SQL Warehouse in Operations Insights categorizes SQL performance:
* Degraded (A): SQL statements with declining performance over time (e.g., increased latency), indicating potential issues.
* Plan changes (C): SQL statements with altered execution plans, impacting performance or resource use.
* Why not B or D?
* Climbing (B): Not a standard category; possibly a misnomer.
* Efficient (D): Positive performance isn't a focus category.
These help identify and resolve SQL issues.
Reference: SQL Warehouse Categories
NEW QUESTION # 88
Which is a valid Log Category name in Oracle Cloud Infrastructure (OCI) Logging Service?
Answer: C
Explanation:
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
NEW QUESTION # 89
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