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NEW QUESTION # 113
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: A
Explanation:
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 # 114
An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals. As a result of the cloned detector, which of the following is true?
Answer: A
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a detector creates a copy of the detector that you can modify without affecting the original detector. You can change the metric, filter, and signal settings of the cloned detector. However, the new signals that you add to the cloned detector will not be reflected in the original detector, nor in the original chart that the detector was based on.
NEW QUESTION # 115
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:
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 purposes. 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 occur. 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 listed.
NEW QUESTION # 116
Which of the following are supported rollup functions in Splunk Observability Cloud?
Answer: B
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions:
Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval.
Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval.
Min: Returns the minimum data point value seen in the MTS reporting interval.
Max: Returns the maximum data point value seen in the MTS reporting interval.
Latest: Returns the most recent data point value seen in the MTS reporting interval.
Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval.
Rate: Returns the rate of change of data points in the MTS reporting interval.
NEW QUESTION # 117
When applying analytic functions to a subset of multiple time series (MTS) in a signal, what is the resulting data structure?
Answer: B
NEW QUESTION # 118
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