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| Section | Weight | Objectives |
|---|---|---|
| Alarms and Notifications | 25% | - Notifications service and integration patterns - Alarm configuration, thresholds, and status |
| OCI Monitoring and Metrics | 30% | - Monitoring service concepts and metrics collection - Metric queries, dashboards, and visualization |
| Logging and Log Analytics | 25% | - OCI Logging service and log management - Logging Analytics: ingestion, parsing, and analytics |
| Application Performance Monitoring and Distributed Tracing | 20% | - APM setup, instrumentation, and end-user monitoring - Distributed tracing, diagnostics, and troubleshooting |
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NEW QUESTION # 31
In OCI Log Analytics, which attribute is used to select parsers for logs collected from OCI services?
Answer: C
Explanation:
An entity type identifies the category of resource that produces the logs, such as an OCI Compute Instance, APM Domain, Load Balancer, Object Storage bucket, or Events service rule. When defining a Log Analytics source, the selected entity type establishes the resource context and helps determine which entities and relevant processing definitions can be associated with that source. The source then includes one or more parsers appropriate for the expected log format. Lookups enrich fields by mapping existing values to additional information, while labels tag records-often to identify problem conditions-during processing.
Fields are the individual values extracted from log entries by a parser. They do not determine which parser should process an OCI service's logs. Among the provided choices, Entity Type is therefore the correct selection attribute. Oracle documentation
NEW QUESTION # 32
In Application Performance Monitoring (APM), where is the span context information located during transfer?
Answer: B
Explanation:
In OCI APM, span context (e.g., Trace ID, Span ID) is propagated across services to track requests.
* In HTTP header (B): Span context is embedded in HTTP headers (e.g., X-B3-TraceId) during transfer between services. This allows APM to correlate spans across distributed systems for a single user request.
* Why not A, C, or D?
* Service boundaries (A): This is a conceptual term, not a location for data.
* HTTP call (C): Too vague-"HTTP call" isn't a specific storage location.
* Browser and microservices (D): Context originates here but is transferred via headers, not stored locally during transit.
This follows the OpenTracing standard used by OCI APM.
Reference: APM Traces and Spans
NEW QUESTION # 33
Which are the different data sources from where the Application Performance Monitoring (APM) Java agent can collect spans and metrics data?
Answer: D
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 # 34
From the following, select the different metric namespaces used for APM.
Answer: D
Explanation:
APM uses specific metric namespaces to categorize its telemetry data:
* oracle_apm_rum, oracle_apm_synthetics, and oracle_apm_monitoring (D):
* oracle_apm_rum: Metrics from Real User Monitoring (e.g., page load times).
* oracle_apm_synthetics: Metrics from Synthetic Monitoring (e.g., test response times).
* oracle_apm_monitoring: Metrics from distributed tracing (e.g., span counts). These namespaces align with APM's three core features.
* Why not A, B, or C?
* A: Individual metric names, not namespaces.
* B: Incomplete and incorrect namespace naming.
* C: Mixes metric types with partial namespace names.
These namespaces enable targeted metric queries in OCI Monitoring.
Reference: APM Metrics
NEW QUESTION # 35
What are the TWO benefits of Observability Lakehouse in Operations Insights? (Choose two.)
Answer: A,B
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 # 36
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