Free Oracle 1z0-1111-25 Practice Exam with Questions & Answers | Set: 2

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.

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.

For analytics, the data source must provide detailed, actionable information.

Database Audit Logs (C): These logs contain rich data like user actions, SQL queries, timestamps, and security events, making them ideal for performance, security, and compliance analysis in Logging Analytics.

Why not A, B, or D?

Entity types (A): These are metadata definitions, not data for analytics.

Log Sources (B): These are configurations for log parsing, not the logs themselves.

Logging Analytic Entities (D): Entities are resource representations, not the data content.

Database Audit Logs offer the depth needed for meaningful insights.

The OCI Events Service enables event-driven automation by reacting to changes in OCI resources. Two valid use cases are:

Process files when they are uploaded in an Object Storage bucket (A): You can create an event rule to trigger an action (e.g., invoking an Oracle Function) when an object is created (com.oraclecloud.objectstorage.createobject). The function could process the file (e.g., image resizing).

Perform cleanup tasks when an OCI resource is terminated (B): An event rule can detect resource termination (e.g., com.oraclecloud.computeapi.terminateinstance.end) and trigger a function to clean up associated resources (e.g., delete volumes).

Why not C or D?

Migrate Events (C): Events Service doesn’t “migrate” events; it triggers actions. Migration is more aligned with Service Connector Hub.

Configuration management (D): This is handled by services like Resource Manager or Ansible, not Events Service.

These use cases showcase the service’s ability to automate workflows based on resource state changes.

In OCI Logging, logs are wrapped in a Unified Envelope:

Unified Envelope (D): Contains metadata like logGroupId (the OCID of the Log Group), timeCreated, and compartmentId. This identifies the Log Group for each log entry.

Why not A, B, or C?

Source Section (A): Indicates log origin, not the group OCID.

Data section (B): Holds the log content, not metadata like OCID.

Oracle section (C): Not a valid log section.

The Unified Envelope standardizes log metadata.

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.

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.

In OCI Logging Analytics, collecting logs for an Entity (a logical representation of a resource like a host or database) requires linking it to a Log Source.

Create an Association of required Log Source with that Entity (B): This is the correct step. An association connects an Entity (e.g., a server) to a Log Source (e.g., Syslog), specifying where and how logs are collected. Once associated, Logging Analytics begins ingestion and parsing.

Why not A, C, or D?

Configure a path (A): The path is part of the Log Source definition, not the act of starting collection.

Identify Fields (C): Field extraction is a post-collection step, not the initiation process.

Enable a Parser (D): Parsers are embedded in Log Sources; enabling them is implicit in the association, not a separate step.

This association is the foundational action to enable log collection.