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| Section | Weight | Objectives |
|---|---|---|
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment |
| Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings - Dimensions, time-series data, and cardinality |
| Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors - Notification rules and incident response |
| Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
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NEW QUESTION # 106
What happens when the limit of allowed dimensions is exceeded for an MTS?
Answer: D
Explanation:
According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS1. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS2. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud2. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.
NEW QUESTION # 107
A customer is sending data from a machine that is over-utilized. Because of a lack of system resources, data points from this machine are often delayed by up to 10 minutes. Which setting can be modified in a detector to prevent alerts from firing before the datapoints arrive?
Answer: D
Explanation:
Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived.
By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points.
In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points arrive, and avoid false positives or missing data.
NEW QUESTION # 108
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: C
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 deployed. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down. 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.
NEW QUESTION # 109
Which of the following statements is true of detectors created from a chart on a custom dashboard?
Answer: D
Explanation:
The correct answer is D. The detector is automatically linked to the chart.
When you create a detector from a chart on a custom dashboard, the detector is automatically linked to the chart. This means that you can see the detector status and alerts on the chart, and you can access the detector settings from the chart menu. You can also unlink the detector from the chart if you want to1 Changes made to the chart do not affect the detector, and changes made to the detector do not affect the chart. The detector and the chart are independent entities that have their own settings and parameters. However, if you change the metric or dimension of the chart, you might lose the link to the detector1 The alerts generated by the detector will show up in the Alerts page, where you can view, manage, and acknowledge them. You can also see them on the team landing page if you assign the detector to a team2 To learn more about how to create and link detectors from charts on custom dashboards, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/link-detectors-to-charts.html 2: https://docs.splunk.com/observability/alerts-detectors-notifications/view-manage-alerts.html
NEW QUESTION # 110
The Sum Aggregation option for analytic functions does which of the following?
Answer: B
Explanation:
Explanation
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is analytic functions. Analytic functions are mathematical operations that can be applied to metrics to transform, aggregate, or analyze them.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Analytic Functions, which explains that analytic functions can be used to perform calculations on metrics, such as sum, average, min, max, count, etc. The document also provides examples of how to use analytic functions in charts and dashboards.
One of the analytic functions that can be used is Sum Aggregation, which calculates the sum of values present in the input time series across the entire environment or per group. The document gives an example of how to use Sum Aggregation to calculate the total CPU usage across all hosts in a group by using the following syntax:
sum(cpu.utilization) by hostgroup
NEW QUESTION # 111
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