Oracle 1Z0-1111-25 Exam Questions

To create a rule in the OCI Events Service, you need to define what triggers the rule and what happens when it's triggered. The two required components are:

Actions (B): These specify the tasks to perform when an event matches the rule (e.g., invoking a function, sending a notification, or streaming to a service). Without an action, the rule has no effect.

Rule Conditions (C): These define the criteria for matching events (e.g., event type like com.oraclecloud.computeapi.launchinstance.end or resource attributes). Conditions filter which events trigger the rule.

Why not A, D, or E?

Management Agent Cloud Service (A): This is unrelated to Events Service rules; it's for monitoring resources.

Install Key (D): This is used for agent installation, not event rules.

Service Connector (E): While it can work with Events Service, it's a separate service and not a required component of an event rule itself.

These two elements form the core of an OCI Events Service rule, enabling event-driven automation.

In distributed tracing within OCI APM:

Root span (C): The first span in a trace, representing the entry point of a user request (e.g., an HTTP request from a browser). It has no parent span and initiates the chain of subsequent spans across services.

Why not A or B?

Ajax call (A): A type of request, not a span term.

Trace ID (B): A unique identifier for the entire trace, not a span.

The root span is foundational to tracing a request's journey.

FluentD is an open-source data collector used for continuous log collection with client-side processing in OCI Logging. Two applicable scenarios are:

Managing apps/services which push logs to Object Storage (A): FluentD can be configured to collect logs from applications or services (e.g., Oracle Functions) that write logs to Object Storage buckets. It processes these logs client-side and forwards them to OCI Logging or Logging Analytics.

Comprehensive monitoring for OKE/Kubernetes (B): FluentD is widely used in Kubernetes environments like Oracle Container Engine for Kubernetes (OKE) to collect logs from pods, containers, and nodes. It processes these logs locally before sending them to OCI services for analysis.

Why not C or D?

Monitoring unsupported systems (C): While possible, this is not a primary FluentD scenario in OCI---it's more about extending Management Agent capabilities.

Log Source (D): This is a component of Logging Analytics, not a FluentD scenario.

FluentD's flexibility makes it ideal for these use cases in OCI's observability ecosystem.

Reducing noise in Logging Analytics improves log analysis focus:

Use parsed logs search (C): Searches based on extracted fields (e.g., severity=ERROR) filter out irrelevant logs, targeting specific issues.

Use time-picker to limit the volume of logs (D): Narrows the time range (e.g., last hour), reducing the dataset to relevant periods.

Why not A or B?

Histogram records (A): Visualizes data distribution, not a noise reduction method.

Specific keywords (B): Useful but less precise than parsed fields; raw text search isn't emphasized in Logging Analytics.

These methods enhance signal-to-noise ratio.

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.