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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Topic 2: Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment |
| Topic 3: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Topic 4: Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors |
| Topic 5: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 6: Visualizing Metrics | 20% | - Using SignalFlow for custom queries - Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series |
| Topic 7: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality - Data retention and resolution settings |
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NEW QUESTION # 80
A customer wants to create different severity alerts on the same signal, where critical alerts are sent to one recipient and major alerts are sent to a different recipient. Which of the following will accomplish this?
Answer: B
Explanation:
Creating two separate rules in the same detector allows each rule to have its own threshold and recipient configuration, enabling critical alerts to go to one recipient and major alerts to another.
NEW QUESTION # 81
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
Answer: C
Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, lag is a rollup function that returns the difference between the most recent and the previous data point values seen in the metric time series reporting interval. This can be used to measure the time delta between a data point being sent and a data point being received, as long as the data points have timestamps that reflect their send and receive times. For example, if a data point is sent at 10:00:00 and received at 10:00:05, the lag value for that data point is 5 seconds.
NEW QUESTION # 82
A customer is experiencing issues getting metrics from a new receiver they have configured in the OpenTelemetry Collector. How would the customer go about troubleshooting further with the logging exporter?
Answer: D
Explanation:
Explanation
The correct answer is B. Adding logging into the metrics receiver pipeline.
The logging exporter is a component that allows the OpenTelemetry Collector to send traces, metrics, and logs directly to the console. It can be used to diagnose and troubleshoot issues with telemetry received and processed by the Collector, or to obtain samples for other purposes1 To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this case, since you are experiencing issues with a new receiver for metrics, you need to add the logging exporter to the metrics receiver pipeline. This will create a new plot that shows the metrics received by the Collector and any errors or warnings that might occur1 The image that you have sent with your question shows how to add the logging exporter to the metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes logging as one of the options.
This means that the metrics received by any of the receivers listed in the receivers section will be sent to the logging exporter as well as to any other exporters listed2 To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2:
https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html
NEW QUESTION # 83
Changes to which type of metadata result in a new metric time series?
Answer: C
Explanation:
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS).
Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host, you will create a new MTS for the same metric name.
Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed.
NEW QUESTION # 84
A user configures a detector with a "Static Threshold" alert condition. What must they define for this condition to trigger correctly?
Answer: A
Explanation:
A Static Threshold condition requires the user to specify a fixed threshold value and how long the signal must remain above or below that value before the detector triggers an alert.
NEW QUESTION # 85
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