Pass the Oracle Oracle Cloud Infrastructure 1z0-1111-25 Questions and answers with CertsForce

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.

In APM, a vantage point is used in:

Synthetic Monitoring (D): Runs tests from specific locations (vantage points) to monitor web application or API availability and performance globally.

Why not A, B, or C?

Java Management (A): Unrelated to vantage points.

Distributed Tracing (B): Tracks internal request flows, not external tests.

Application Insights (C): Not a formal APM feature; vague term.

Vantage points simulate user access from different regions.

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.

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.

In OCI Stack Monitoring, once Management Agents are deployed and resources (e.g., databases, applications) are discovered, the immediate next step is the collection of metric data.

Metric data is immediately collected (A): Management Agents are lightweight processes that continuously collect performance and health metrics from discovered resources (e.g., CPU usage, memory utilization) and send them to OCI services like Monitoring or Stack Monitoring. This data becomes available for visualization and analysis right after discovery.

Why not B, C, or D?

Alarm rules (B): Alarms are configured separately in the OCI Monitoring service and only trigger after metric data is collected and thresholds are breached—not an immediate post-discovery action.

Resource discovery (C): Discovery happensbeforethis stage, as the question assumes resources are already discovered. Agents don’t rediscover resources post-setup.

Notifications (D): Notifications require separate configuration (e.g., via the Notifications service) and are not an automatic outcome of agent setup and discovery.

This aligns with Stack Monitoring’s purpose of providing real-time visibility into resource performance.

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 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.

The Trace Explorer in OCI Application Performance Monitoring (APM) is a tool for analyzingdistributed traces and spans. Its key functions include:

View the details of specific spans (A): Trace Explorer allows users to drill into individual spans within a trace, displaying details such as duration, status, tags, logs, and errors. This helps identify performance bottlenecks or failures in specific service calls.

Select pre-defined queries for common use cases (B): It provides built-in queries (e.g., slowest traces, error traces, traces by service) to quickly filter and analyze common scenarios, enhancing troubleshooting efficiency.

Why not C or D?

Display status of monitored systems (C): System status is monitored via OCI Monitoring or Stack Monitoring, not Trace Explorer, which focuses on traces.

Define custom metrics for traces (D): Custom metrics are defined in OCI Monitoring, not Trace Explorer, which is for viewing, not creating metrics.

Trace Explorer enhances visibility into distributed application performance.

In OCI Monitoring’s Monitoring Query Language (MQL), Resolution affects data aggregation:

Resolution controls the total length of each time window (A): It specifies the time interval (e.g., 1m for 1 minute) over which metric data is aggregated (e.g., averaged, summed), determining query granularity.

Why not B, C, or D?

B: Start time is set by the query’s time range, not Resolution.

C: Resolution doesn’t affect alarm states; that’s a separate mechanism.

D: Suppression is an alarm feature, unrelated to Resolution.

Resolution fine-tunes metric analysis precision.

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.