1z0-1111-25 Oracle Cloud Infrastructure 2025 Observability Professional Questions and Answers

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.

Operations Insights provides:

Capacity Planning (A): Forecasts resource needs based on historical trends and scenarios.

Exadata Insights (D): Analyzes Exadata system performance and capacity.

Why not B or C?

Database Fleet Monitoring (B): A goal, not a named capability; covered under broader features.

Entity Topology Viewing (C): More aligned with Stack Monitoring, not Operations Insights.

These capabilities optimize resource management.

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.

Management Agents require group membership and policies to interact with OCI services:

AgentUsersGrp (A): A predefined dynamic group for all Management Agents in a tenancy. With an attached policy granting upload permissions (e.g., allow dynamic-group AgentUsersGrp to use discovery-service-family in tenancy), agents can send data to the discovery service for resource detection.

Why not B, C, or D?

B and C: Specific to Stack Monitoring roles, not general agent uploads.

D: A generic name, not a predefined OCI group.

AgentUsersGrp is the standard for this purpose.

OCI Audit logs track API operations for security and compliance.

Invalid statement: Audit Logs are disabled by default (B): Audit Logs are enabled by default across all compartments in a tenancy—no manual activation is required. They automatically record all API activities.

Why A and C are valid:

Security-related events (A): Audit Logs capture user actions, making them critical for security monitoring.

Compartment-level display (C): Logs can be filtered and viewed by compartment or tenancy level via the Console or API.

Audit Logs are always active, with a 90-day retention period by default.

MQL expressions in OCI Monitoring retrieve and process metric data. Optional components include:

Statistic (B): Specifies how to summarize data (e.g., avg, max). If omitted, raw data is returned.

Grouping Function (D): Aggregates data across metric streams (e.g., by resourceId). It’s optional if no grouping is needed.

Why not A, C, E, or F?

Interval (A): Defines the time window; defaults apply if omitted, but it’s typicallyrequired for meaningful queries.

Previous (C): Not a standard MQL component (possibly a typo).

Metric (E): Mandatory to specify what’s being queried.

Dimensions (F): Filters data; required if specific streams are targeted, though optional in basic queries.

Statistic and Grouping Function enhance flexibility but aren’t mandatory.

To investigate user activity and REST API calls over the last six hours, the OCI Audit service is the appropriate tool.

Audit (E): This service automatically records all API operations (including REST API calls) performed on OCI resources. It provides detailed logs with user details, timestamps, and actions, ideal for security and compliance investigations. You can filter audit logs by time range (e.g., last six hours) and user attributes.

Why not A, B, C, or D?

Notifications (A): Sends alerts but doesn’t store or analyze API call details.

Service Connectors (B): Moves data between services, not for auditing.

Management Agent (C): Collects metrics/logs from resources, not API audit data.

Logging (D): Handles application and system logs, not API activity tracking.

Audit logs are retained for 90 days by default, making this a perfect fit.

The correct OCI CLI syntax for searching logs must include the compartment, log group, log, and query:

B: oci logging-search search-logs --search-query 'search "ocid1.compartment.oc1..aaaaaaaawqegmjifhni77bqm625cxioavoq775jckfn2syxqtmglabcccdxyz/web/application" | where data.statusCode = 400' --time-start 2022-02-06T00:00:00Z --time-end 2022-02-07T00:00:00Z

Specifies the compartment OCID, web Log Group, and application Log in the scope path.

Filters for data.statusCode = 400 using proper syntax (= instead of space).

Includes valid UTC time range.

Why not A, C, or D?

A: Missing log group/log; incorrect filter syntax (400 without =).

C: No filter for status code 400.

D: Incorrect command (oci log instead of oci logging-search); redundant parameters.

B aligns with OCI Logging’s search syntax.

To archive logs from OCI Logging Service to Object Storage, an automated data movement solution is needed:

Service Connector (A): Part of Service Connector Hub, this capability moves data between OCI services. A Service Connector can be configured with Logging as the source and Object Storage as the target, automatically transferring logs based on filters (e.g., log level) or schedules.

Why not B, C, or D?

ObjectCollectionRule (B): Used in Logging Analytics to ingest logsfromObject Storage, not archivetoit.

IAM policy (C): Governs permissions but doesn’t perform data movement.

Logging Query (D): Analyzes logs within Logging Service, not for archiving.

Service Connector ensures seamless, scalable log archiving.

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 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.

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.

Operations Insights collects telemetry data for analysis from:

Management Agent (C): A lightweight process that gathers metrics (e.g., CPU, memory) from hosts, databases, or applications and sends them to Operations Insights.

Enterprise Manager (D): An on-premises tool that provides database performance and configuration data to Operations Insights.

Why not A or B?

OCI Streams (A): A streaming service, not a telemetry source for Operations Insights.

OCI Functions (B): Serverless compute, not a direct telemetry source.

These sources enable comprehensive resource monitoring.

The Management Agent Cloud Service collects and transports data from resources to OCI services. Let’s evaluate:

Invalid statement: Can only transport data into AWS or GCS (A): This is false. Management Agents transport data to OCI services (e.g., Logging Analytics, Monitoring) or custom OCI endpoints, not AWS or Google Cloud Storage (GCS).

Why B, C, and D are valid:

Self-monitored (B): Agents monitor their own health and report to OCI.

Transports to OCI services (C): Supports Logging Analytics, Monitoring, and custom OCI endpoints.

On-demand operations (D): Allows tasks like metric collection or log uploads on demand.

Management Agents are OCI-centric, not limited to external clouds.

To enable Stack Monitoring:

Dynamic group for discovery service (A): A dynamic group defines resources (e.g., compute instances) that Stack Monitoring can discover and monitor. A policy granting permissions to this group is also required.

Why not B or C?

Machine Learning group (B): Not a valid OCI concept for Stack Monitoring.

User group for VCN collection (C): User groups manage human access, not service discovery.

This setup ensures Stack Monitoring can access and monitor resources.