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

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.

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.

An MQL query for an alarm must evaluate a condition:

(CpuUtilization[1m].max() > 80).grouping().sum() (B):

CpuUtilization[1m]: Queries CPU utilization over 1-minute intervals.

.max(): Takes the maximum value per interval.

> 80: Sets a threshold (80%).

.grouping().sum(): Aggregates across metric streams, triggering the alarm if the sum exceeds 80%.This is a complete alarm query.

Why not A, C, or D?

A: Filters by shape but lacks a threshold or alarm condition.

C: Missing condition for alarming.

D: No threshold; case sensitivity doesn’t match OCI convention.

B ensures threshold-based alerting.

The Log Explorer UI in OCI Logging Analytics includes four key interaction elements: Fields Panel, Time Picker, Scope Filter, and Results Panel. These allow users to search, filter, and analyze logs interactively.

Dashboard (D): This is not part of the Log Explorer UI. Dashboards are separate visualizations in Logging Analytics for summarizing data, not an interactive element of the Log Explorer.

Why A, B, and C are key elements:

Fields Panel (A): Displays log fields for filtering and analysis.

Time Picker (B): Sets the time range for log queries.

Scope Filter (C): Defines the scope (e.g., compartments, log groups) of the log search.

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.

OCI Application Performance Monitoring (APM) provides tools to monitor application performance:

Distributed Tracing (A): Tracks requests across microservices, showing latency and dependencies via traces and spans.

Real User Monitoring (C): Captures real user interactions with web applications (e.g., page load times) using a Browser Agent.

Why not B or D?

Capacity Planning (B): Available in Operations Insights, not APM.

Java Management (D): Not a feature of APM; Java Agent is a tool, not a feature.

These features align with APM’s focus on performance and user experience.

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.

Logging Analytics is a core O&M service:

Monitor, aggregate, index, and analyze log data (A): Collects logs from OCI and external sources, indexes them for search, and provides analytics (e.g., clustering) to monitor and troubleshoot systems.

Why not B or C?

B: Log centralization occurs, but “subscription model” isn’t a feature.

C: Resource creation is an Events Service use case, not Logging Analytics.

This is its primary observability role.

To collect logs from on-premises instances and archive Info-level logs in OCI Object Storage, you need tools for log ingestion and data movement:

Service Connectors (A): This feature enables data transfer from OCI Logging (source) to Object Storage (target). You can configure a service connector with a filter (e.g., log level = Info) to archive only Info-level logs.

ObjectCollection Rule (D): Part of Logging Analytics, this rule collects logs from Object Storage buckets into Logging Analytics for analysis. If logs are first written to Object Storage by an agent, this rule ensures continuous ingestion.

Why not B or C?

Agent Configuration (B): Used to set up Management Agents but doesn’t handle archiving to Object Storage.

Cloud Agent Plugin Grouping Function (C): This is not a valid OCI feature.

The workflow involves agents sending logs to Logging, Service Connectors filtering and moving them to Object Storage, and ObjectCollection Rules enabling further analysis.

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.

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.

In OCI APM’s Synthetic Monitoring, Vantage Points are locations from which synthetic tests (e.g., HTTP requests) are run. Dedicated Vantage Points are private, user-managed instances, distinct from public ones hosted by Oracle:

Applications on-premise or on secured network cannot be tested from a public Vantage Point (B): Public Vantage Points, located in Oracle-managed regions, lack access to private networks (e.g., on-premise servers or firewalled applications). Dedicated Vantage Points, deployed within a user’s network, overcome this limitation.

Test internal customer applications (C): Dedicated Vantage Points enable testing of internal applications (e.g., intranet sites) not exposed to the public internet, ensuring performance monitoring from within the secured environment.

Why not A or D?

Test from public Vantage Point (A): Contradicts B; public Vantage Points can’t access private networks.

Test Deployment Manager and Scheduler (D): These are unrelated OCI components, not Synthetic Monitoring targets.

Dedicated Vantage Points extend monitoring to restricted environments.

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 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 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.