Free 1Z0-1111-25 sample questions

Correct answer: Implement a custom sampling filter in the APM agent configuration to specifically target the payment service endpoint and set its sampling rate to 100%.

OCI APM agents support custom sampling filters that allow for granular control over trace collection. By defining a filter that targets the specific payment service (e.g., by URL path or service name) and setting its rate to 100 (always sample), you can ensure complete trace capture for that critical service while allowing other services to use the default sampling rate. This is the most precise and efficient method to meet the requirement.

Correct answer: The native auto-parsing capabilities for unstructured text which automatically discover and extract fields.

OCI Logging Analytics has powerful built-in machine learning capabilities that can automatically parse unstructured logs. It recognizes common patterns like key-value pairs, JSON snippets, and other structured data within free text, and automatically extracts them as fields. This eliminates the need for manually creating and maintaining complex regex or Grok parsers, which directly addresses the team's requirement.

Correct answer: The `timeShift()` function to compare the current metric stream with a past metric stream.

The `timeShift()` function in MQL is specifically designed for this purpose. It allows you to shift a metric stream back in time, enabling direct comparison between current data and historical data (e.g., '1w' for one week ago). The SRE can then construct a query that calculates the ratio or difference between the current request count and the time-shifted request count to trigger the alarm when the drop exceeds 50%.

Correct answers: OCI Service Connector Hub; OCI Events

Correct answer: True

This statement is true. A core feature of OCI Stack Monitoring is its ability to run discovery jobs via the Management Agent to identify supported resource types (like WebLogic, Oracle DB, and hosts) and automatically determine the relationships between them to build a comprehensive stack topology.

Correct answer: The event rule's condition is too specific and does not match the event JSON payload.

The most common reason for an Events rule not firing is a mismatch between the rule's conditions and the actual event's JSON data. This could be due to a typo in an attribute name, an incorrect value, or a misunderstanding of the event's structure. The administrator should inspect the raw JSON of a recent instance creation event and compare it meticulously against the rule's matching conditions.

Correct answer: Metrics are aggregatable numeric data about system health (the 'what'), Logs are immutable, timestamped records of discrete events (the 'why'), and Traces show the end-to-end journey of a request through a distributed system (the 'where').

This option correctly defines the role of each pillar. Metrics (OCI Monitoring) provide quantifiable data on system performance. Logs (OCI Logging/Logging Analytics) provide detailed, event-specific context for troubleshooting. Traces (OCI APM) provide a causal chain of events as a request flows through multiple services, which is crucial for debugging in microservices architectures.

Correct answer: Injecting the APM Browser Agent (a JavaScript snippet) into the HTML pages of the website.

Real User Monitoring (RUM) works by collecting performance data directly from the end-user's browser. To achieve this, OCI APM provides a JavaScript snippet (the Browser Agent) that must be embedded into the website's HTML ` ` section. This script then collects and reports client-side performance metrics, JavaScript errors, and AJAX call details back to the APM service.

Correct answer: Using the Link feature to correlate logs based on common fields and time proximity.

The Link feature in Logging Analytics is designed for this exact use case. It allows the administrator to start with a specific log entry (the `ORA-00600` error) and then automatically finds and groups other log entries from various sources (like application logs, OS logs, etc.) that occurred around the same time and share common contextual fields (e.g., `host`, `db_instance_id`). This creates a transaction-like view of the problem, dramatically speeding up root cause analysis.

Correct answer: Create an alarm targeting the `my_app_metrics` namespace and the `app_health_status` metric. Set the trigger rule condition to `value < 1` with a trigger delay of 5 minutes.

The correct approach is to target the custom namespace (`my_app_metrics`) and the specific metric (`app_health_status`). The condition `value < 1` correctly identifies the unhealthy state (when the value is 0). Using a 'trigger delay minutes' of 5 ensures the alarm only enters the firing state if the condition remains true for the entire 5-minute duration, preventing false positives from transient issues.