Master Oracle 1Z0-1111-25 Exam with Reliable Practice Questions

Correct : B

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.

Correct : A, B

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.

Correct : D

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.

Correct : B

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.

Correct : A, D

Exadata Warehouse Insights in OCI Operations Insights provides advanced analytics to forecast resource usage for Exadata systems.

Memory (A): Tracks and predicts memory utilization based on historical trends, aiding capacity planning.

CPU (D): Forecasts CPU usage, helping identify potential bottlenecks or over-provisioning.

Why not B or C?

Network usage (B): While monitored, it's not a primary focus of Exadata Warehouse Insights' future usage predictions.

AIOps (C): This is a methodology, not a resource usage metric.

These forecasts leverage historical data and what-if analysis for proactive management.