Free Practice Questions for Oracle 1z0-1111-25 Exam

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.

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.

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.

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.

Connecting to an external database via OCI’s External Database service requires specific connection details:

DNS hostname, database name, connection type and management agent OCID, user credentials, and role (C):

DNS hostname: The fully qualified domain name of the database host.

Database name: Unique identifier of the database.

Connection type: Protocol (e.g., JDBC, OCI).

Management agent OCID: Identifier of the agent on the host.

User credentials: Username and password.

Role: Privilege level (e.g., SYSDBA).These are the minimum required fields for secure connectivity.

Why not A, B, or D?

A: Missing DNS hostname, critical for locating the host.

B: Missing DNS hostname; port alone isn’t sufficient.

D: Includes extras (e.g., port, network protocol) that are optional or inferred.

This ensures accurate database connectivity.

For analytics, the data source must provide detailed, actionable information.

Database Audit Logs (C): These logs contain rich data like user actions, SQL queries, timestamps, and security events, making them ideal for performance, security, and compliance analysis in Logging Analytics.

Why not A, B, or D?

Entity types (A): These are metadata definitions, not data for analytics.

Log Sources (B): These are configurations for log parsing, not the logs themselves.

Logging Analytic Entities (D): Entities are resource representations, not the data content.

Database Audit Logs offer the depth needed for meaningful insights.

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.

In Logging Analytics, ML-driven visualizations aid log analysis:

Cluster (A): Uses machine learning to group logs by structural similarity (“shape”), reducing noise and highlighting patterns or anomalies in large datasets.

Why not B or C?

Summary Table (B): Aggregates data tabularly, not ML-based or shape-focused.

Word Cloud (C): Displays word frequency, not structural grouping.

Cluster is ideal for large-scale log pattern recognition.