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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Topic 2: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings - Dimensions, time-series data, and cardinality |
| Topic 3: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Topic 4: Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Topic 5: Data Collection & Ingestion | 15% | - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment - Signal flow and data processing pipeline |
| Topic 6: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Topic 7: Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Notification rules and incident response - Common use cases: latency, saturation, errors |
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NEW QUESTION # 73
A user wants to see related dashboards, runbooks, or external documentation directly from a chart. Which feature should they configure?
Answer: B
Explanation:
Data Links allow users to configure contextual links from charts or dashboards to external resources, such as runbooks, documentation, or related dashboards, streamlining the troubleshooting workflow.
NEW QUESTION # 74
What is the finest resolution that Calendar Window Transformation datapoints can be calculated and displayed?
Answer: D
Explanation:
Calendar Window Transformation datapoints are calculated and displayed at the native resolution of the input time series, ensuring that the aggregation respects the original granularity of the metric data.
NEW QUESTION # 75
A customer has a very dynamic infrastructure. During every deployment, all existing instances are destroyed, and new ones are created Given this deployment model, how should a detector be created that will not send false notifications of instances being down?
Answer: D
Explanation:
According to the web search results, ephemeral infrastructure is a term that describes instances that are auto-scaled up or down, or are brought up with new code versions and discarded or recycled when the next code version is deployed1. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down2. To use this feature, you need to do the following steps:
Create the detector as usual, by selecting the metric or dimension that you want to monitor and alert on, and choosing the alert condition and severity level.
Select Alert settings, then select Ephemeral Infrastructure. This will enable a special mode for the detector that will automatically clear alerts for instances that are expected to be terminated.
Enter the expected lifetime of an instance in minutes. This is the maximum amount of time that an instance is expected to live before being replaced by a new one. For example, if your instances are replaced every hour, you can enter 60 minutes as the expected lifetime.
Save the detector and activate it.
With this feature, the detector will only trigger alerts when an instance stops reporting a metric unexpectedly, based on its expected lifetime. If an instance stops reporting a metric within its expected lifetime, the detector will assume that it was terminated on purpose and will not trigger an alert. Therefore, option B is correct.
NEW QUESTION # 76
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?
Answer: C
Explanation:
Explanation
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 77
Which chart type in Splunk Observability Cloud is best suited for visualizing the proportion of time a resource spends in different states?
Answer: B
Explanation:
A Heatmap is well-suited for visualizing many time series at once and showing how values (such as states or utilization levels) are distributed across a large number of instances over time.
NEW QUESTION # 78
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