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.

OCI Dashboards consolidate the three pillars of observability:

Logs, Metrics, and Traces (A):

Logs: Event records from Logging Service.

Metrics: Numeric data from Monitoring Service.

Traces: Request flows from APM.These can be visualized together in OCI Dashboards for unified observability.

Why not B, C, or D?

B: Resource categories, not pillars.

C: Tools or components, not pillars.

D: OCI services, not the core concepts.

This integration enhances system visibility.

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.

SQL Warehouse in Operations Insights categorizes SQL performance:

Degraded (A): SQL statements with declining performance over time (e.g., increased latency), indicating potential issues.

Plan changes (C): SQL statements with altered execution plans, impacting performance or resource use.

Why not B or D?

Climbing (B): Not a standard category; possibly a misnomer.

Efficient (D): Positive performance isn’t a focus category.

These help identify and resolve SQL issues.

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.

The auto-upgrade feature for Management Agents ensures they stay current:

Users cannot disable "auto-upgrade" (B): Auto-upgrade is mandatory and enabled by default, automatically updating agents to the latest version for security and functionality. Users lack an opt-out mechanism.

Why not A, C, or D?

A: Incorrect; it’s not configurable at the tenancy level.

C: True that it’s enabled by default, but B is more precise about the lack of disable option.

D: False; auto-upgrade exists.

This ensures consistent agent reliability.

In OCI Logging Analytics, Labels enhance log analysis by tagging specific records:

Labels (B): Predefined tags applied during ingestion based on conditions (e.g., “Error” for logs with “error” or “exception”). Labels help categorize and filter problem logs for easier searching and troubleshooting.

Why not A, C, or D?

Entity Type (A): Defines the resource type (e.g., host), not a tagging mechanism.

Log Origin (C): Metadata about log source, not for problem identification.

Extended Fields (D): Custom fields extracted post-ingestion, not tags applied during ingestion.

Labels streamline issue detection at the ingestion stage.

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.

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.

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.

An APM Domain in OCI defines the monitoring scope for APM:

A resource type containing the systems monitored by APM (B): An APM Domain is a logical container for monitored systems (e.g., microservices, web servers, databases). It groups these resources for trace and metric collection, often separated by environment (e.g., dev, prod).

Why not A, C, or D?

Users, roles, identity (A): Relates to IAM, not APM Domains.

High-availability connectivity (C): Infrastructure concern, not an APM Domain’s purpose.

Compartment (D): Compartments organize resources; APM Domains are specific to monitored systems within them.

APM Domains structure monitoring efforts effectively.

Database Management Service provides advanced database oversight:

Compare database performance across different time periods or perform real-time monitoring of SQL statements (C): Uses Performance Hub for historical and real-time SQL monitoring.

Analyze and tune SQL performance issues of Oracle Databases on-premises, OCI, and multi-cloud environments (D): Offers SQL tuning across diverse deployments.

Why not A or B?

A and B: Capacity forecasting is an Operations Insights feature, not Database Management.

These actions enhance database performance management.

For continuous log ingestion from Object Storage:

ObjectCollection Rule (A): A Logging Analytics feature that monitors Object Storage buckets and ingests logs based on defined patterns (e.g., bucket name, object prefix). It’s designed for this purpose.

Why not B, C, or D?

Object Store (B), Object Storage (C), Object Store Bucket (D): These refer to the storage service or its components, not a method for log ingestion.

ObjectCollection Rule ensures automated, ongoing log collection.

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.

The solution must address continuous monitoring and event-driven notifications:

D:

OCI Compute agent on on-premises VMs and OCI instances: Ensures metric continuity (e.g., CPU, memory) across the migration, using Management Agents for both environments.

Events service: Tracks launchinstance.end for deployment completion and createbucket for new buckets.

Notifications and Events: Sends email alerts for these events.

Why not A, B, or C?

A: Misses on-premises monitoring continuity.

B: Lacks bucket creation tracking.

C: Redundant 3rd-party tool use; OCI agents suffice.

D provides end-to-end coverage.